Showing 1–50 of 114 results for author: Marques, M A L

Quantum geometry and critical temperature enhancement in MgB$_2$ superconductivity

Authors: Yi Jiang, Haoyu Hu, Dumitru Călugăru, Kaja H. Hiorth, Junze Deng, Hanqi Pi, Handong Chen, Maia G. Vergniory, Ion Errea, Emilia Morosan, Leslie M. Schoop, Claudia Felser, Miguel A. L. Marques, Päivi Törmä, Daniel Agterberg, B. Andrei Bernevig

Abstract: MgB$_2$, a phonon-mediated superconductor with record-high critical temperature $T_c\simeq 39$ K, is revisited to obtain a comprehensive theory of electrons, phonons, and their coupling with minimal ab initio input. We construct compact analytic models for the electronic structure, phonons, and electron-phonon coupling (EPC) of MgB$_2$. We show that strong in-plane B $sp^2$ bonding realizes an obs… ▽ More MgB$_2$, a phonon-mediated superconductor with record-high critical temperature $T_c\simeq 39$ K, is revisited to obtain a comprehensive theory of electrons, phonons, and their coupling with minimal ab initio input. We construct compact analytic models for the electronic structure, phonons, and electron-phonon coupling (EPC) of MgB$_2$. We show that strong in-plane B $sp^2$ bonding realizes an obstructed band structure whose natural description is a bond-centered kagome lattice, yielding small quasi-2D $σ$-band Fermi-surface cylinders and pronounced quantum-geometric effects. The phonon spectrum is found to closely track that of a graphene-like boron layer, but the heavy intercalated Mg atoms dominate the three acoustic branches and rigidly lift the boron modes into the optical sector, while the in-plane B-B bond-stretching mode exhibits a pronounced softening along $Γ$-A. By symmetry, this $Γ$-point bond-stretching mode is the only $Γ$ phonon that can couple to the $σ$ Fermi surface, explaining its dominant contribution to the EPC. Upon electron doping toward the doubly degenerate band edge of the $σ$ sheets, we find that a reduced density of states competes with enhanced EPC matrix elements. At light electron doping, ab initio calculations show that the EPC enhancement dominates, leading to an increase in $T_c$ (within the clean doping limit without disorder effects). Using the Gaussian approximation for the EPC tensor, we further show that this enhancement is overwhelmingly quantum geometric in origin, arising from a geometric EPC contribution of the small $σ$ Fermi surface peaked at $Γ$. Overall, our results provide a transparent, symmetry-based account of superconductivity in MgB$_2$ and suggest that quantum-geometric effects can be essential for shaping doping trends in phonon-mediated superconductors. △ Less

Submitted 21 July, 2026; originally announced July 2026.

Comments: 96 pages, 38 figures

Machine Learning Materials Properties by Encoding Orbital-Projected Density of States

Authors: Paulo Pires, Pierre-Paul De Breuck, Mauro Fava, Hai-Chen Wang, Miguel A. L. Marques

Abstract: Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node features has received comparatively little attention, and conventional approaches rely on static elemental descriptors that carry no information about the quantum-mechanical electronic environment of each atom in its crystal… ▽ More Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node features has received comparatively little attention, and conventional approaches rely on static elemental descriptors that carry no information about the quantum-mechanical electronic environment of each atom in its crystalline host. Here we show that augmenting atomic node representations with site-projected orbital density of states (pDOS) fingerprints, computed directly from density functional theory calculations, yields systematic and substantial improvements in predictive performance.These representations are fused with Pettifor elemental embeddings at each atomic site before message passing. For the superconducting critical temperature $T_c$ and the optical dielectric constant $ε_{\infty}$,the pDOS augmentation reduces prediction errors by 22.9% and 27.9%, respectively, relative to the elemental-descriptor baseline. These improvements are comparable to those achieved by doubling the training-set size. The gains are, however, contingent on training-set size. For the magnetic exchange energies of Heusler compounds, a substantially smaller dataset, the improvement is reduced,indicating that pDOS augmentation is most effective when the training data exceeds the length of the pDOS feature vector. We introduce an interpretable spectral attention-gating mechanism that reveals that the model autonomously learns to prioritize the orbital channels and energy windows most physically relevant to each target property. These results establish pDOS-augmented graph nodes as a broadly applicable strategy for infusing first-principles electronic-structure knowledge into graph networks, opening a practical route to high-accuracy property prediction in data-scarce regimes. △ Less

Submitted 8 July, 2026; originally announced July 2026.

High-throughput study of electrical conductivity in ordered metals

Authors: Thalis H. B. da Silva, Hai-Chen Wang, Tiago F. T. Cerqueira, Simone Di Cataldo, Silvana Botti, Miguel A. L. Marques

Abstract: We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum (36.59 x $10^6$ S/m). We perform full electron-phonon coupling calculations for the top-performing materials, yiel… ▽ More We present a computational framework that integrates machine learning with high-throughput ab initio calculations to screen over 2.8 million compounds for metallic transport. We identify several intermetallic candidates with predicted high conductivities comparable to that of aluminum (36.59 x $10^6$ S/m). We perform full electron-phonon coupling calculations for the top-performing materials, yielding results in good agreement with available experimental data. Our analysis reveals that while the noble metals (Ag, Au, Cu) possess a conductivity that remains difficult to surpass due to their unique electronic structure and low scattering, compounds like LiBePt2 can achieve comparable performance by utilizing valence electrons from light elements to shift high-scattering d-states beneath the Fermi level. This study not only identifies novel high-performance conductors but also demonstrates the predictive power of combining statistical learning with detailed ab initio calculations. △ Less

Submitted 15 August, 2026; v1 submitted 21 May, 2026; originally announced May 2026.

Ab-initio superfluid weight and superconducting penetration depth

Authors: Kaja H. Hiorth, Martin Gutierrez-Amigo, Théo Cavignac, Kristjan Haule, Miguel A. L. Marques, Päivi Törmä

Abstract: Machine learning and high-throughput screening approaches to superconductor discovery require physically meaningful descriptors that capture essential physics while remaining computationally tractable. The superfluid weight is an ideal descriptor as it is a prerequisite for superconductivity, determines the magnetic penetration depth and the Berezinskii-Kosterlitz-Thouless transition temperature i… ▽ More Machine learning and high-throughput screening approaches to superconductor discovery require physically meaningful descriptors that capture essential physics while remaining computationally tractable. The superfluid weight is an ideal descriptor as it is a prerequisite for superconductivity, determines the magnetic penetration depth and the Berezinskii-Kosterlitz-Thouless transition temperature in two-dimensional materials, may limit the critical temperature in unconventional superconductors through phase coherence, and reveals quantum geometric contributions to supercurrent transport. We develop a computationally efficient framework for calculating the zero-temperature, mean-field superfluid weight for uniform pairing from density functional theory band structures and Bloch wavefunctions. We separately evaluate the conventional contribution from band curvature and the geometric contribution from quantum geometry. To validate the method, we calculate London penetration depths for a few conventional superconductors (Al, Pb, Nb, MgB$_2$, LuRu$_3$B$_2$ and YRu$_3$B$_2$) and find good agreement with experiment after accounting for nonlocal corrections, strong-coupling effects, and sample quality. The conventional contribution dominates by orders of magnitude in these wide-band materials, as expected. This framework provides a foundation for large-scale screening of superconducting candidates and exploring quantum geometric effects in unconventional superconductors. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Comments: 12 pages, 3 figures. Submitted to Physical Review B as a regular article on the 2nd of March 2026

Semi-Local Exchange-Correlation Approximations in Density Functional Theory

Authors: Fabien Tran, Susi Lehtola, Stefano Pittalis, Miguel A. L. Marques

Abstract: Density functional theory has become the workhorse of modern electronic structure calculations, with wide-ranging applications in chemistry, physics, materials science, biochemistry, etc. At its heart lies the exchange-correlation functional, a quantity which exactly encapsulates the many-body effects stemming from the quantum mechanical interactions between the electrons. Yet, the exact functiona… ▽ More Density functional theory has become the workhorse of modern electronic structure calculations, with wide-ranging applications in chemistry, physics, materials science, biochemistry, etc. At its heart lies the exchange-correlation functional, a quantity which exactly encapsulates the many-body effects stemming from the quantum mechanical interactions between the electrons. Yet, the exact functional is unknown, and computationally tractable approximations are therefore necessary for practical applications. Over the past six decades, hundreds of density functional approximations have been proposed with varying accuracy and computational efficiency. This review surveys the theoretical foundations of semi-local functionals, including local density approximations, generalized gradient approximations, and meta-generalized gradient approximations. We provide a comprehensive, consistently organized discussion that consolidates both historical developments and recent advances in this field. Beginning with the essentials of Kohn-Sham density functional theory, we present the construction principles of semi-local exchange-correlation functionals. Special attention is given to the physical motivations underlying functional development, the mathematical properties that guide their construction, and the practical considerations that determine their applicability across different chemical and physical systems. This work is intended to serve as both an introduction for newcomers to the field and a comprehensive reference for practitioners. By consolidating the extensive literature on semi-local functionals and providing a unified framework for their construction and application, we aim to facilitate further developments in density functional approximations and their use in tackling the diverse challenges of modern computational chemistry and condensed matter physics. △ Less

Submitted 12 June, 2026; v1 submitted 19 February, 2026; originally announced February 2026.

Comments: 202 pages, 2 figures

Machine Learning-Guided Discovery of Kagome Superconductors YRu3B2 and LuRu3B2

Authors: Rose Albu Mustaf, Sajilesh K. P., Sanu Mishra, Junze Deng, Yi Jiang, Kaja H. Hiorth, Eeli O. Lamponen, Martin Gutierrez-Amigo, Päivi Törmä, Miguel A. L. Marques, B. Andrei Bernevig, Emilia Morosan

Abstract: We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughput screening combined with first principles calculations. These materials crystallize in the hexagonal CeCo$_3$B$_2$-type structure with planar kagome networks formed by Ru atoms. We observe supercondu… ▽ More We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughput screening combined with first principles calculations. These materials crystallize in the hexagonal CeCo$_3$B$_2$-type structure with planar kagome networks formed by Ru atoms. We observe superconducting critical temperatures of $T_{c} = 0.81$~K for YRu$_3$B$_2$ and $T_{c} = 0.95$~K for LuRu$_3$B$_2$, confirmed through magnetization and specific heat measurements. Both compounds exhibit nearly 100\% superconducting volume fractions, demonstrating bulk superconductivity. Compared with LaRu$_3$Si$_2$, YRu$_3$B$_2$ and LuRu$_3$B$_2$ show a more dispersive Ru local $d_{x^2-y^2}$ quasi-flat band (and thus a reduced DOS at $E_F$) together with an overall hardening of the phonon spectrum, both of which lower the electron-phonon coupling (EPC) constant $λ$. Meanwhile, the dominant real-space EPC between Ru local $d_{x^2-y^2}$ states and the low-frequency Ru in-plane local $x$ branch remains nearly unchanged, indicating that the reduction of $λ$ originates from the $d_{x^2-y^2}$ DOS reduction and the overall phonon hardening. Superfluid weight calculations show that conventional contributions dominate over quantum geometric effects due to the dispersive nature of bands near the Fermi level. This work demonstrates the effectiveness of integrating machine learning screening, first principles theory, and experimental synthesis for accelerating the discovery of new superconducting materials. △ Less

Submitted 4 February, 2026; v1 submitted 16 December, 2025; originally announced December 2025.

AI-Driven Expansion and Application of the Alexandria Database

Authors: Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck, Antoine Loew, Tiago F. T. Cerqueira, Hai-Chen Wang, Anton Bochkarev, Yury Lysogorskiy, Aldo H. Romero, Ralf Drautz, Silvana Botti, Miguel A. L. Marques

Abstract: We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we… ▽ More We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses. △ Less

Submitted 1 May, 2026; v1 submitted 9 December, 2025; originally announced December 2025.

High-Tc superconductivity above 130 K in cubic MH4 compounds at ambient pressure

Authors: Xinxin Li, Weishuo Xu, Zengguang Zhou, Jingming Shi, Hanyu Liu, Yue-Wen Fang, Wenwen Cui, Yinwei Li, Miguel A. L. Marques

Abstract: Hydrides have long been considered promising candidates for achieving room-temperature superconductivity; however, the extremely high pressures typically required for high critical temperatures remain a major challenge in experiment. Here, we propose a class of high-Tc ambient-pressure superconductors with MH4 stoichiometry. These hydrogen-based compounds adopt the bcc PtHg4 structure type, in whi… ▽ More Hydrides have long been considered promising candidates for achieving room-temperature superconductivity; however, the extremely high pressures typically required for high critical temperatures remain a major challenge in experiment. Here, we propose a class of high-Tc ambient-pressure superconductors with MH4 stoichiometry. These hydrogen-based compounds adopt the bcc PtHg4 structure type, in which hydrogen atoms occupy the one-quarter body-diagonal sites of metal lattices, with the metal atoms acting as chemical templates for hydrogen assembly. Through comprehensive first-principles calculations, we identify three promising superconductors, PtH4, AuH4 and PdH4, with superconducting critical temperatures of 84 K, 89 K, and 133 K, respectively, all surpassing the liquid-nitrogen temperature threshold of 77 K. The remarkable superconducting properties originate from strong electron-phonon coupling associated with hydrogen vibrations, which in turn arise from phonon softening in the mid-frequency range. Our results provide crucial insights into the design of high-Tc superconductors suitable for future experiments and applications at ambient pressure. △ Less

Submitted 6 November, 2025; originally announced November 2025.

Comments: 9 pages, 5 figures

Generative AI for Crystal Structures: A Review

Authors: Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese, Silvana Botti, Miguel A. L. Marques

Abstract: As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of gen… ▽ More As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials. △ Less

Submitted 2 September, 2025; originally announced September 2025.

Comments: 23 pages, 4 figures, 3 tables

MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy

Authors: Nitik Bhatia, Ondrej Krejci, Silvana Botti, Patrick Rinke, Miguel A. L. Marques

Abstract: Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that simultaneously span broad chemical diversity and provide reliable uncertainty estimates has limited their wider applicability. In this work, we introduce MACE4IRmol, an uncertainty-aware foundation model ensemble built o… ▽ More Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that simultaneously span broad chemical diversity and provide reliable uncertainty estimates has limited their wider applicability. In this work, we introduce MACE4IRmol, an uncertainty-aware foundation model ensemble built on the MACE architecture. MACE4IRmol is trained on ~16 million molecular geometries and the corresponding density-functional theory (DFT) energies, forces, and dipole moments from the QCML dataset. The training data encompasses approximately 80 elements and a diverse set of molecules, including organic and inorganic compounds, and metal complexes. Importantly, MACE4IRmol is formulated as an ensemble of models to enable uncertainty quantification, which helps improve robustness in chemically diverse systems. Within this ensemble, separate models are trained with and without explicit dispersion corrections, allowing systematic assessment of van der Waals effects. In addition, MACE4IRmol delivers accurate predictions of energies, forces, dipole moments, and infrared spectra at a fraction of the computational cost of DFT, while enabling the explicit inclusion of nuclear quantum effects in infrared spectrum simulations. By combining generality, accuracy, efficiency, and uncertainty estimation, MACE4IRmol opens the door to rapid and reliable infrared spectra prediction for complex and diverse molecular systems. △ Less

Submitted 9 March, 2026; v1 submitted 26 August, 2025; originally announced August 2025.

Comments: 36 pages and 8 figures

Universal Machine Learning Potentials under Pressure

Authors: Antoine Loew, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques

Abstract: Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one such significant gap by systematically investigating the accuracy of uMLIPs under extreme pressure co… ▽ More Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one such significant gap by systematically investigating the accuracy of uMLIPs under extreme pressure conditions from 0 to 150 GPa. Our benchmark reveals that while these models excel at standard pressure, their predictive accuracy deteriorates considerably as pressure increases. This decline in performance originates from fundamental limitations in the training data rather than in algorithmic constraints. In fact, we show that through targeted fine-tuning on high-pressure configurations, the robustness of the models can be easily increased. These findings underscore the importance of identifying and addressing overlooked regimes in the development of the next generation of truly universal interatomic potentials. △ Less

Submitted 25 August, 2025; originally announced August 2025.

Universal Machine Learning Potential for Systems with Reduced Dimensionality

Authors: Giulio Benedini, Antoine Loew, Matti Hellstrom, Silvana Botti, Miguel A. L. Marques

Abstract: We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that w… ▽ More We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for three-dimensional systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01-0.02 angstrom and errors in the energy below 10~meV/atom across all dimensionalities. These results demonstrate that state-of-the-art universal machine learning interatomic potentials have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces. △ Less

Submitted 21 August, 2025; originally announced August 2025.

Accelerating point defect photo-emission calculations with machine learning interatomic potentials

Authors: Kartikeya Sharma, Antoine Loew, Haiyuan Wang, Fredrik A. Nilsson, Manjari Jain, Miguel A. L. Marques, Kristian S. Thygesen

Abstract: We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio accuracy. By replacing the costly density functional theory (DFT) computation of phonon modes with much faster MLIP phonon mode calculations, our approach achieves spee… ▽ More We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio accuracy. By replacing the costly density functional theory (DFT) computation of phonon modes with much faster MLIP phonon mode calculations, our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss. We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states. The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface. Across all the systems, we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes. This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity, opening pathways to high-throughput screening of defect-engineered materials. Our work not only demonstrates accelerated calculation of PL spectra with DFT accuracy, but also makes such calculations tractable for more complex materials. △ Less

Submitted 8 September, 2025; v1 submitted 2 May, 2025; originally announced May 2025.

Comments: 9 pages, 5 figures, 1 table

Theory of Superconductivity in LaRu$_3$Si$_2$ and Predictions of New Kagome Flat Band Superconductors

Authors: Junze Deng, Yi Jiang, Tiago F. T. Cerqueira, Haoyu Hu, Eeli O. Lamponen, Dumitru Călugăru, Hanqi Pi, Zhijun Wang, Maia G. Vergniory, Emilia Morosan, Titus Neupert, S. Blanco-Canosa, Claudia Felser, Kristjan Haule, Miguel A. L. Marques, Päivi Törmä, B. Andrei Bernevig

Abstract: We present a comprehensive investigation of the flat-band kagome superconductor LaRu$_3$Si$_2$, which has recently been reported to host charge density wave (CDW) order above room temperature ($T_{CDW} \simeq 400$ K). The stable crystal structure above the CDW transition is identified via soft phonon condensation and confirmed to be harmonically stable through ab initio calculations, consistent wi… ▽ More We present a comprehensive investigation of the flat-band kagome superconductor LaRu$_3$Si$_2$, which has recently been reported to host charge density wave (CDW) order above room temperature ($T_{CDW} \simeq 400$ K). The stable crystal structure above the CDW transition is identified via soft phonon condensation and confirmed to be harmonically stable through ab initio calculations, consistent with recent X-ray diffraction refinements. The electron-phonon coupling (EPC) in LaRu$_3$Si$_2$ is found to be mode-selective, primarily driven by strong interactions between Ru-$B_{3u}$ phonons (local $x$-direction, pointing toward the hexagon center) and Ru-$A_g$ electrons (local $d_{x^2-y^2}$ orbital) within the kagome lattice. Using a spring-ball model, we identify this mode-selective EPC as a universal feature of kagome materials. Employing the newly developed Gaussian approximation of the hopping parameters, we derive an analytical expression for the EPC and demonstrate that superconductivity in LaRu$_3$Si$_2$ is mostly driven by the coupling between the kagome $B_{3u}$ phonons and the $A_g$ electrons. The impact of doping is also investigated, revealing that light hole doping (approximately one hole per unit cell) significantly enhances the superconducting critical temperature $T_c$ by 50%, whereas heavy doping induces structural instability and ferromagnetism. Furthermore, high-throughput screening identifies 3063 stable 1:3:2 kagome materials, of which 428 are predicted to exhibit superconductivity with $T_c > 1$ K, and the highest $T_c$ reaching 15 K. These findings establish LaRu$_3$Si$_2$ and related materials as promising platforms for exploring the interplay among kagome flat bands, EPC, and superconductivity. Additionally, they may offer valuable insights into potential limitations on the $T_c$ of flat-band superconductivity in real materials. △ Less

Submitted 26 March, 2025; originally announced March 2025.

Comments: 8+60 pages, 3+32 figures, 2+9 tables

High-throughput study of kagome compounds in the AV3Sb5 family

Authors: Thalis H. B. da Silva, Tiago F. T. Cerqueira, Hai-Chen Wang, Miguel A. L. Marques

Abstract: The kagome lattice has emerged as a fertile ground for exotic quantum phenomena, including superconductivity, charge density waves, and topologically nontrivial states. While AV3Sb5 (A = K, Rb, Cs) compounds have been extensively studied in this context, the broader AB3C5 family remains largely unexplored. In this work, we employ machine-learning-accelerated, high-throughput density functional the… ▽ More The kagome lattice has emerged as a fertile ground for exotic quantum phenomena, including superconductivity, charge density waves, and topologically nontrivial states. While AV3Sb5 (A = K, Rb, Cs) compounds have been extensively studied in this context, the broader AB3C5 family remains largely unexplored. In this work, we employ machine-learning-accelerated, high-throughput density functional theory calculations to systematically investigate the stability and electronic properties of kagome materials derived from atomic substitutions in the AV3Sb5 structure. We identify 36 promising candidates that are thermodynamically stable, with many more close to the convex hull. Stable compounds are not only found with a pnictogen (Sb or Bi) as the C atom but also with Au, Hg, Tl, and Ce. This diverse chemistry opens the way to tune the electronic properties of the compounds. In fact, many of these compounds exhibit Dirac points, Van Hove singularities, or flat bands close to the Fermi level. Our findings provide an array of compounds for experimental synthesis and further theoretical exploration of kagome superconductors beyond the already known systems. △ Less

Submitted 21 March, 2025; originally announced March 2025.

Comments: 9 figures in the main text, 37 in the supplemental. 7 pages in the main text, 11 pages in the supplemental. The paper has been submitted to npj Quantum Materials

The Maximum $T_c$ of Conventional Superconductors at Ambient Pressure

Authors: Kun Gao, Tiago F. T. Cerqueira, Antonio Sanna, Yue-Wen Fang, Đorđe Dangić, Ion Errea, Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques

Abstract: The theoretical maximum critical temperature ($T_c$) for conventional superconductors at ambient pressure remains a fundamental question in condensed matter physics. Through analysis of electron-phonon calculations for over 20,000 metals, we critically examine this question. We find that while hydride metals can exhibit maximum phonon frequencies of more than 5000 K, the crucial logarithmic averag… ▽ More The theoretical maximum critical temperature ($T_c$) for conventional superconductors at ambient pressure remains a fundamental question in condensed matter physics. Through analysis of electron-phonon calculations for over 20,000 metals, we critically examine this question. We find that while hydride metals can exhibit maximum phonon frequencies of more than 5000 K, the crucial logarithmic average frequency $ω_\text{log}$ rarely exceeds 1800 K. Our data reveals an inherent trade-off between $ω_\text{log}$ and the electron-phonon coupling constant $λ$, suggesting that the optimal Eliashberg function that maximizes $T_c$ is unphysical. Based on our calculations, we identify Li$_2$AgH$_6$ and its sibling Li$_2$AuH$_6$ as theoretical materials that likely approach the practical limit for conventional superconductivity at ambient pressure. Analysis of thermodynamic stability indicates that compounds with higher predicted $T_c$ values are increasingly unstable, making their synthesis challenging. While fundamental physical laws do not strictly limit $T_c$ to low-temperatures, our analysis suggests that achieving room-temperature conventional superconductivity at ambient pressure is extremely unlikely. △ Less

Submitted 25 February, 2025; originally announced February 2025.

Journal ref: Nat Commun 16, 8253 (2025)

A generative material transformer using Wyckoff representation

Authors: Pierre-Paul De Breuck, Hashim A. Piracha, Gian-Marco Rignanese, Miguel A. L. Marques

Abstract: Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on inv… ▽ More Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and space groups and can be conditioned on specific properties. We demonstrate its ability to generate stable, novel, and unique crystal structures by conditioning on the distance to the convex hull. Resulting structures are 8 times more likely to be stable than baselines using PyXtal with charge compensation, while maintaining high computational efficiency. We also release a dataset of 3 million unique crystals generated by our method, including 4,000 compounds verified by density-functional theory to be within 0.001 eV/atom of the convex hull. △ Less

Submitted 29 January, 2025; v1 submitted 27 January, 2025; originally announced January 2025.

Comments: 14 pages, 10 figures

Universal Machine Learning Interatomic Potentials are Ready for Phonons

Authors: Antoine Loew, Dewen Sun, Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques

Abstract: There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties, which are critical for under-standing… ▽ More There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models for predictingenergy, forces, and stresses, combining innovative architectures with big data. Here, we benchmarkthese models on their ability to predict harmonic phonon properties, which are critical for under-standing the vibrational and thermal behavior of materials. Using around 10 000 ab initio phononcalculations, we evaluate model performance across various phonon-related parameters to test theuniversal applicability of these models. The results reveal that some models achieve high accuracyin predicting harmonic phonon properties. However, others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamicalequilibrium. These findings highlight the importance of considering phonon-related properties inthe development of universal machine learning interatomic potentials. △ Less

Submitted 8 May, 2025; v1 submitted 21 December, 2024; originally announced December 2024.

Prediction of high-Tc superconductivity in ternary actinium beryllium hydrides at low pressure

Authors: Kun Gao, Wenwen Cui, Jingming Shi, Artur P. Durajski, Jian Hao, Silvana Botti, Miguel A. L. Marques, Yinwei Li

Abstract: Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significantly limit their practical applications. An effective strategy to reduce the external pressure is to add a light element M that binds with H to form MHx units, acting as a chemical precompressor. We exemplify this idea by… ▽ More Hydrogen-rich superconductors are promising candidates to achieve room-temperature superconductivity. However, the extreme pressures needed to stabilize these structures significantly limit their practical applications. An effective strategy to reduce the external pressure is to add a light element M that binds with H to form MHx units, acting as a chemical precompressor. We exemplify this idea by performing ab initio calculations of the Ac-Be-H phase diagram, proving that the metallization pressure of Ac-H binaries, for which critical temperatures as high as 200 K were predicted at 200 GPa, can be significantly reduced via beryllium incorporation. We identify three thermodynamically stable (AcBe2H10, AcBeH8, and AcBe2H14) and four metastable compounds (fcc AcBeH8, AcBeH10, AcBeH12 and AcBe2H16). All of them are superconductors. In particular, fcc AcBeH8 remains dynamically stable down to 10 GPa, where it exhibits a superconducting transition temperature Tc of 181 K. The Be-H bonds are responsible for the exceptional properties of these ternary compounds and allow them to remain dynamically stable close to ambient pressure. Our results suggest that high-Tc superconductivity in hydrides is achievable at low pressure and may stimulate experimental synthesis of ternary hydrides. △ Less

Submitted 6 August, 2025; v1 submitted 28 November, 2024; originally announced November 2024.

Journal ref: Physical Review B 109,014501(2024)

Ambient pressure high temperature superconductivity in RbPH$_3$ facilitated by ionic anharmonicity

Authors: Đorđe Dangić, Yue-Wen Fang, Tiago F. T. Cerqueira, Antonio Sanna, Miguel A. L. Marques, Ion Errea

Abstract: Recent predictions of metastable high-temperature hydride superconductors give hope that superconductivity at ambient conditions is within reach. In this work, we predict RbPH$_3$ as a new compound with a superconducting critical temperature around 100 K at ambient pressure, dynamically stabilized thanks to ionic quantum anharmonic effects. RbPH$_3$ is thermodynamically stable at 30 GPa in a perov… ▽ More Recent predictions of metastable high-temperature hydride superconductors give hope that superconductivity at ambient conditions is within reach. In this work, we predict RbPH$_3$ as a new compound with a superconducting critical temperature around 100 K at ambient pressure, dynamically stabilized thanks to ionic quantum anharmonic effects. RbPH$_3$ is thermodynamically stable at 30 GPa in a perovskite $Pm\bar{3}m$ phase, allowing its experimental synthesis at moderate pressures far from the megabar regime. With lowering pressure it is expected to transform to a $R3m$ phase that should stay dynamically stable thanks to quantum fluctuations down to ambient pressures. Both phases are metallic, with the $R3m$ phase having three distinct Fermi surfaces, composed mostly of states with phosphorus and hydrogen character. The structures are held together by strong P-H covalent bonds, resembling the pattern observed in the high-temperature superconducting H$_3$S, with extra electrons donated by rubidium. These results demonstrate that quantum ionic fluctuations, neglected thus far in high-throughput calculations, can stabilize at ambient pressure hydride superconductors with a high critical temperature. △ Less

Submitted 6 November, 2024; originally announced November 2024.

In-architecture X-ray assisted C-Br dissociation for on-surface fabrication of diamondoid chains

Authors: Yan Wang, Niklas Grabicki, Hibiki Orio, Juan Li, Jie Gao, Xiaoxi Zhang, Tiago F. T. Cerqueira, Miguel A. L. Marques, Zhaotan Jiang, Friedrich Reinert, Oliver Dumele, Carlos-Andres Palma

Abstract: The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis approaches. Here we demonstrate through scann… ▽ More The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions represents an effective strategy in this direction, enabling reactivity exploration and on-surface synthesis approaches. Here we demonstrate through scanning probe microscopy, time-of-flight mass spectrometry and photoelectron spectroscopy, that self-assembled layers of dibromodiamantanes on gold can be debrominated at atomic wavelengths (Al K$α$ at 8.87 $\mathring{A}$ and Mg K$α$ at 9.89 $\mathring{A}$) and low temperatures without affecting their well-defined arrangement. The resulting 'in-architecture' debromination enables the fabrication of diamantane chains from self-assembled precursors in close proximity, which is otherwise inaccessible through annealing on metal surfaces. Our work introduces a novel approach for the fabrication of nanodiamond chains, with significant implications for in-architecture and layer-by-layer synthesis. △ Less

Submitted 25 October, 2024; originally announced October 2024.

A non-orthogonal representation of the chemical space

Authors: Tiago F. T. Cerqueira, Haichen Wang, Silvana Botti, Miguel A. L. Marques

Abstract: We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemic… ▽ More We present a novel approach to generate a fingerprint for crystalline materials that balances efficiency for machine processing and human interpretability, allowing its application in both machine learning inference and understanding of structure-property relationships. Our proposed material encoding has two components: one representing the crystal structure and the other characterizing the chemical composition, that we call Pettifor embedding. For the latter we construct a non-orthogonal space where each axis represents a chemical element and where the angle between the axes quantifies a measure of the similarity between them. The chemical composition is then defined by the point on the unit sphere in this non-orthogonal space. We show that the Pettifor embeddings systematically outperform other commonly used elemental embeddings in compositional machine learning models. Using the Pettifor embeddings to define a distance metric and applying dimension reduction techniques, we construct a two-dimensional global map of the space of thermodynamically stable crystalline compounds. Despite their simplicity, such maps succeed in providing a physical separation of material classes according to basic physical properties. △ Less

Submitted 20 March, 2025; v1 submitted 28 June, 2024; originally announced June 2024.

Searching Materials Space for Hydride Superconductors at Ambient Pressure

Authors: Tiago F. T. Cerqueira, Yue-Wen Fang, Ion Errea, Antonio Sanna, Miguel A. L. Marques

Abstract: We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition temperatures surpassing 20 K, and some even reaching above 70 K. These compounds have very different crystal structures, with different dimensionality, chemical compo… ▽ More We employed a machine-learning assisted approach to search for superconducting hydrides under ambient pressure within an extensive dataset comprising over 150 000 compounds. Our investigation yielded around 50 systems with transition temperatures surpassing 20 K, and some even reaching above 70 K. These compounds have very different crystal structures, with different dimensionality, chemical composition, stoichiometry, and arrangement of the hydrogens. Interestingly, most of these systems displayed slight thermodynamic instability, implying that their synthesis would require conditions beyond ambient equilibrium. Moreover, we found a consistent chemical composition in the majority of these systems, which combines alkali or alkali-earth elements with noble metals. This observation suggests a promising avenue for future experimental investigations into high-temperature superconductivity within hydrides at ambient pressure. △ Less

Submitted 20 March, 2024; originally announced March 2024.

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Authors: Matthew L. Evans, Johan Bergsma, Andrius Merkys, Casper W. Andersen, Oskar B. Andersson, Daniel Beltrán, Evgeny Blokhin, Tara M. Boland, Rubén Castañeda Balderas, Kamal Choudhary, Alberto Díaz Díaz, Rodrigo Domínguez García, Hagen Eckert, Kristjan Eimre, María Elena Fuentes Montero, Adam M. Krajewski, Jens Jørgen Mortensen, José Manuel Nápoles Duarte, Jacob Pietryga, Ji Qi, Felipe de Jesús Trejo Carrillo, Antanas Vaitkus, Jusong Yu, Adam Zettel, Pedro Baptista de Castro , et al. (34 additional authors not shown)

Abstract: The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 relea… ▽ More The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development. △ Less

Submitted 5 April, 2024; v1 submitted 1 February, 2024; originally announced February 2024.

Journal ref: Digital Discovery, 2024, 3, 1509-1533

Two-Dimensional Noble Metal Chalcogenides in the Frustrated Snub-Square Lattice

Authors: Hai-Chen Wang, Ahmad W. Huran, Miguel A. L. Marques, Muralidhar Nalabothula, Ludger Wirtz, Zachary Romestan, Aldo H. Romero

Abstract: We study two-dimensional noble metal chalcogenides, with composition {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semi-regular two-dimensional tesselation formed by triangles and squares that exhibits geometrical frustration. We use for comparison a square lattice, from which the snub-square tiling can be derived by a simple rotation of the squares. The mono-layer sn… ▽ More We study two-dimensional noble metal chalcogenides, with composition {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semi-regular two-dimensional tesselation formed by triangles and squares that exhibits geometrical frustration. We use for comparison a square lattice, from which the snub-square tiling can be derived by a simple rotation of the squares. The mono-layer snub-square chalcogenides are very close to thermodynamic stability, with the most stable system (Ag2Se) a mere 7 meV/atom above the convex hull of stability. All compounds studied in the square and snub-square lattice are semiconductors, with band gaps ranging from 0.1 to more than 2.5 eV. Excitonic effects are strong, with an exciton binding energy of around 0.3 eV. We propose the Cu (001) surface as a possible substrate to synthesize Cu2Se, although many other metal and semiconducting surfaces can be found with very good lattice matching. △ Less

Submitted 18 October, 2023; originally announced October 2023.

Prediction of Ambient Pressure Conventional Superconductivity above 80K in Thermodynamically Stable Hydride Compounds

Authors: Antonio Sanna, Tiago F. T. Cerqueira, Yue-Wen Fang, Ion Errea, Alfred Ludwig, Miguel A. L. Marques

Abstract: The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials were identified by scrutinizing more th… ▽ More The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow. They are thermodynamically stable, indicating that they are serious candidates for experimental synthesis. We predict that their superconducting transition temperatures are in the range of 45-80K, or even above 100K with appropriate electron doping of the Pt compound. These results indicate that, although very rare, high-temperature superconductivity in thermodynamically stable hydrides is achievable at room pressure. △ Less

Submitted 10 October, 2023; originally announced October 2023.

Comments: 9 pages, 6 figures

Journal ref: npj Comput Mater 10, 44 (2024)

Sampling the Whole Materials Space for Conventional Superconducting Materials

Authors: Tiago F. T. Cerqueira, Antonio Sanna, Miguel A. L. Marques

Abstract: We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconduct… ▽ More We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (Tc ) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 545 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2 , which we predict to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has been previously synthesized in this phase, further heightening its potential for practical applications. △ Less

Submitted 20 July, 2023; originally announced July 2023.

Reproducibility of density functional approximations: how new functionals should be reported

Authors: Susi Lehtola, Miguel A. L. Marques

Abstract: Density functional theory is the workhorse of chemistry and materials science, and novel density functional approximations (DFAs) are published every year. To become available in program packages, the novel DFAs need to be (re)implemented. However, according to our experience as developers of Libxc [Lehtola et al, SoftwareX 7, 1 (2018)], a constant problem in this task is verification, due to the… ▽ More Density functional theory is the workhorse of chemistry and materials science, and novel density functional approximations (DFAs) are published every year. To become available in program packages, the novel DFAs need to be (re)implemented. However, according to our experience as developers of Libxc [Lehtola et al, SoftwareX 7, 1 (2018)], a constant problem in this task is verification, due to the lack of reliable reference data. As we discuss in this work, this lack has lead to several non-equivalent implementations of functionals such as BP86, PW91, PBE, and B3LYP across various program packages, yielding different total energies. Through careful verification, we have also found many issues with incorrect functional forms in recent DFAs. The goal of this work is to ensure the reproducibility of DFAs: DFAs must be verifiable in order to prevent reappearances of the abovementioned errors and incompatibilities. A common framework for verification and testing is therefore needed. We suggest several ways in which reference energies can be produced with free and open source software, either with non-self-consistent calculations with tabulated atomic densities or via self-consistent calculations with various program packages. The employed numerical parameters -- especially, the quadrature grid -- need to be converged to guarantee a $\lesssim0.1μE_{h}$ precision in the total energy, which is nowadays routinely achievable in fully numerical calculations. Moreover, as such sub-$μE_{h}$ level agreement can only be achieved when fully equivalent implementations of the DFA are used, also the source code of the reference implementation should be made available in any publication describing a new DFA. △ Less

Submitted 23 August, 2023; v1 submitted 14 July, 2023; originally announced July 2023.

Comments: 16 pages, 1 figure

Journal ref: J. Chem. Phys. 159, 114116 (2023)

Searching for ductile superconducting Heusler X2YZ compounds

Authors: Noah Hoffmann, Tiago F. T. Cerqueira, Pedro Borlido, Antonio Sanna, Jonathan Schmidt, Miguel A. L. Marques

Abstract: Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be superconducting. With this in mind, we perform an extensive study of the superconducting and elastic properties of the cubic (full-)Heusler family. Starting fr… ▽ More Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetallic ductile compounds, and some of them have been found to be superconducting. With this in mind, we perform an extensive study of the superconducting and elastic properties of the cubic (full-)Heusler family. Starting from thermodynamically stable compounds, we use ab initio methods for the calculation of the phonon spectra, electron-phonon couplings, superconducting critical temperatures and elastic tensors. By analyzing the statistical distributions of these properties and comparing them to anti-perovskites we recognize universal behaviors that should be common to all conventional superconductors while others turn out to be specific to the material family. The resulting data is used to train interpretable and predictive machine learning models, that are used to extend our knowledge of superconductivity in Heuslers and to provide an interpretation of our results. In total, we discover a total of 8 hypothetical materials with critical temperatures above 10 K, to be compared with the current record of Tc = 4.7 K in this family. Furthermore, we expect most of these materials to be highly ductile, making them potential candidates for the manufacture of wires and tapes for superconducting magnets. △ Less

Submitted 7 June, 2023; originally announced June 2023.

Structure prediction and characterization of CuI-based ternary $p$-type transparent conductors

Authors: Michael Seifert, Tomáš Rauch, Miguel A. L. Marques, Silvana Botti

Abstract: Zincblende copper iodide has attracted significant interest as a potential material for transparent electronics, thanks to its exceptional light transmission capabilities in the visible range and remarkable hole conductivity. However, remaining challenges hinder the utilization of copper iodide's unique properties in real-world applications. To address this, chalcogen doping has emerged as a viabl… ▽ More Zincblende copper iodide has attracted significant interest as a potential material for transparent electronics, thanks to its exceptional light transmission capabilities in the visible range and remarkable hole conductivity. However, remaining challenges hinder the utilization of copper iodide's unique properties in real-world applications. To address this, chalcogen doping has emerged as a viable approach to enhance the hole concentration in copper iodide. In search of further strategies to improve and tune the electronic properties of this transparent semiconductor, we investigate the ternary phase diagram of copper and iodine with sulphur or selenium by performing structure prediction calculations using the minima hopping method. As a result, we find 11 structures located on or near the convex hull, 9 of which are unreported. Based on our band structure calculations, it appears that sulphur and selenium are promising candidates for achieving ternary semiconductors suitable as $p$-type transparent conducting materials. Additionally, our study reveals the presence of unreported phases that exhibit intriguing topological properties. These findings broaden the scope of potential applications for these ternary systems, highlighting the possibility of harnessing their unique electronic characteristics in diverse electronic devices and systems. △ Less

Submitted 24 May, 2023; originally announced May 2023.

Transfer learning on large datasets for the accurate prediction of material properties

Authors: Noah Hoffmann, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques

Abstract: Graph neural networks trained on large crystal structure databases are extremely effective in replacing ab initio calculations in the discovery and characterization of materials. However, crystal structure datasets comprising millions of materials exist only for the Perdew-Burke-Ernzerhof (PBE) functional. In this work, we investigate the effectiveness of transfer learning to extend these models t… ▽ More Graph neural networks trained on large crystal structure databases are extremely effective in replacing ab initio calculations in the discovery and characterization of materials. However, crystal structure datasets comprising millions of materials exist only for the Perdew-Burke-Ernzerhof (PBE) functional. In this work, we investigate the effectiveness of transfer learning to extend these models to other density functionals. We show that pre-training significantly reduces the size of the dataset required to achieve chemical accuracy and beyond. We also analyze in detail the relationship between the transfer-learning performance and the size of the datasets used for the initial training of the model and transfer learning. We confirm a linear dependence of the error on the size of the datasets on a log-log scale, with a similar slope for both training and the pre-training datasets. This shows that further increasing the size of the pre-training dataset, i.e. performing additional calculations with a low-cost functional, is also effective, through transfer learning, in improving machine-learning predictions with the quality of a more accurate, and possibly computationally more involved functional. Lastly, we compare the efficacy of interproperty and intraproperty transfer learning. △ Less

Submitted 6 March, 2023; originally announced March 2023.

Effects of hole doping on the electronic and optical properties of transparent conducting copper iodide

Authors: Michael Seifert, Miguel A. L. Marques, Silvana Botti

Abstract: Zincblende copper iodide has been attracting growing interest as p-type semiconductor for applications in transparent electronics and transparent thermoelectrics. A key step towards technological applications is the possibility to enhance copper iodide (CuI) conductivity by doping without deteriorating transparency. A recent high-throughput computational study revealed that chalcogen substitutions… ▽ More Zincblende copper iodide has been attracting growing interest as p-type semiconductor for applications in transparent electronics and transparent thermoelectrics. A key step towards technological applications is the possibility to enhance copper iodide (CuI) conductivity by doping without deteriorating transparency. A recent high-throughput computational study revealed that chalcogen substitutions on iodine sites can act as shallow acceptors. Following computational predictions, doping by oxygen, sulfur and selenium substitutions on iodine sites has recently been realized in the laboratory, showing however that few experimental challenges have still to be tackled on the way to technological applications. We investigate here by means of {\it ab initio} calculations the effect of such substitutions on the electronic and optical properties of CuI. Our results suggest that sulfur and selenium doping are the best candidates to obtain a controllable increase of hole concentrations, while preserving at the same time transparency in the visible and high hole mobility. △ Less

Submitted 21 December, 2022; originally announced December 2022.

Symmetry-based computational search for novel binary and ternary 2D materials

Authors: Hai-Chen Wang, Jonathan Schmidt, Miguel A. L. Marques, Ludger Wirtz, Aldo H. Romero

Abstract: We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for… ▽ More We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form A$_n$B$_m$ or A$_n$B$_m$C$_k$. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250~meV/atom above the convex hull of thermodynamic stability. △ Less

Submitted 22 April, 2023; v1 submitted 7 December, 2022; originally announced December 2022.

Homogeneous Electron Liquid in Arbitrary Dimensions: Exchange and Correlation Using the Singwi-Tosi-Land-Sjölander Approach

Authors: L. V. Duc Pham, Pascal Sattler, Miguel A. L. Marques, Carlos L. Benavides-Riveros

Abstract: The ground states of the homogeneous electron gas and the homogeneous electron liquid are cornerstones in quantum physics and chemistry. They are archetypal systems in the regime of slowly varying densities in which the exchange-correlation energy can be estimated with a myriad of methods. For high densities, the behavior of the energy is well-known for 1, 2, and 3 dimensions. Here, we extend this… ▽ More The ground states of the homogeneous electron gas and the homogeneous electron liquid are cornerstones in quantum physics and chemistry. They are archetypal systems in the regime of slowly varying densities in which the exchange-correlation energy can be estimated with a myriad of methods. For high densities, the behavior of the energy is well-known for 1, 2, and 3 dimensions. Here, we extend this model to arbitrary integer dimensions, and compute its correlation energy beyond the random phase approximation (RPA), using the celebrated approach developed by Singwi, Tosi, Land, and Sjölander (STLS), which is known to be remarkably accurate in the description of the full electronic density response for $2D$ and $3D$, both in the paramagnetic and ferromagnetic ground states. For higher dimensions, we compare the results obtained for the correlation energy using the STLS method with the values previously obtained using RPA. We found that at high dimensions STLS tends to be more physical in the sense that the infamous sum rules are better satisfied by the theory. We furthermore illustrate the importance of the plasmon contribution to STLS theory. △ Less

Submitted 1 March, 2023; v1 submitted 6 October, 2022; originally announced October 2022.

Comments: 14 pages, 9 figures

Journal ref: New J. Phys. 25, 083040 (2023)

Large-scale machine-learning-assisted exploration of the whole materials space

Authors: Jonathan Schmidt, Noah Hoffmann, Hai-Chen Wang, Pedro Borlido, Pedro J. M. A. Carriço, Tiago F. T. Cerqueira, Silvana Botti, Miguel A. L. Marques

Abstract: Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing a… ▽ More Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to perform machine-learning assisted high-throughput materials searches including 2500 binary and ternary structure prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and ~150000 compounds with a distance of less than 50 meV/atom from the hull. Combining again machine learning and ab-initio methods, we finally evaluate the discovered materials for applications as superconductors, superhard materials, and we look for candidates with large gap deformation potentials, finding several compounds with extreme values of these properties. △ Less

Submitted 2 October, 2022; originally announced October 2022.

Efficient and improved prediction of the band offsets at semiconductor heterojunctions from meta-GGA density functionals

Authors: Arghya Ghosh, Subrata Jana, Tomáš Rauch, Fabien Tran, Miguel A. L. Marques, Silvana Botti, Lucian A. Constantin, Manish K. Niranjan, Prasanjit Samal

Abstract: Accurate theoretical prediction of the band offsets at interfaces of semiconductor heterostructures can often be quite challenging. Although density functional theory has been reasonably successful to carry out such calculations and efficient and accurate semilocal functionals are desirable to reduce the computational cost. In general, the semilocal functionals based on the generalized gradient ap… ▽ More Accurate theoretical prediction of the band offsets at interfaces of semiconductor heterostructures can often be quite challenging. Although density functional theory has been reasonably successful to carry out such calculations and efficient and accurate semilocal functionals are desirable to reduce the computational cost. In general, the semilocal functionals based on the generalized gradient approximation (GGA) significantly underestimate the bulk band gaps. This, in turn, results in inaccurate estimates of the band offsets at the heterointerfaces. In this paper, we investigate the performance of several advanced meta-GGA functionals in the computational prediction of band offsets at semiconductor heterojunctions. In particular, we investigate the performance of r2SCAN (revised strongly-constrained and appropriately-normed functional), rMGGAC (revised semilocal functional based on cuspless hydrogen model and Pauli kinetic energy density functional), mTASK (modified Aschebrock and Kümmel meta-GGA functional), and LMBJ (local modified Becke-Johnson) exchange-correlation functionals. Our results strongly suggest that these meta-GGA functionals for supercell calculations perform quite well, especially, when compared to computationally more demanding GW calculations. We also present band offsets calculated using ionization potentials and electron affinities, as well as band alignment via the branch point energies. Overall, our study shows that the aforementioned meta-GGA functionals can be used within the DFT framework to estimate the band offsets in semiconductor heterostructures with predictive accuracy. △ Less

Submitted 27 July, 2022; originally announced July 2022.

Many recent density functionals are numerically ill-behaved

Authors: Susi Lehtola, Miguel A. L. Marques

Abstract: Most computational studies in chemistry and materials science are based on the use of density functional theory. Although the exact density functional is unknown, several density functional approximations (DFAs) offer a good balance of affordable computational cost and semi-quantitative accuracy for applications. The development of DFAs still continues on many fronts, and several new DFAs aiming f… ▽ More Most computational studies in chemistry and materials science are based on the use of density functional theory. Although the exact density functional is unknown, several density functional approximations (DFAs) offer a good balance of affordable computational cost and semi-quantitative accuracy for applications. The development of DFAs still continues on many fronts, and several new DFAs aiming for improved accuracy are published every year. However, the numerical behavior of these DFAs is an often overlooked problem. In this work, we look at all 592 DFAs for three-dimensional systems available in Libxc 5.2.2 and examine the convergence of the density functional total energy based on tabulated atomic Hartree-Fock wave functions. We show that several recent DFAs, including the celebrated SCAN family of functionals, show impractically slow convergence with typically used numerical quadrature schemes, making these functionals unsuitable both for routine applications or high-precision studies, as thousands of radial quadrature points may be required to achieve sub-$μE_{h}$ accurate total energies for these unctionals, while standard quadrature grids like the SG-3 grid only contain $\mathcal{O}(100)$ radial quadrature points. These results are both a warning to users to lways check the sufficiency of the quadrature grid when adopting novel functionals, as well as a guideline to the theory community to develop better behaved density functionals. △ Less

Submitted 26 September, 2022; v1 submitted 28 June, 2022; originally announced June 2022.

Comments: 16 pages, 6 figures

Journal ref: J. Chem. Phys. 157, 174114 (2022)

Machine-learning correction to density-functional crystal structure optimization

Authors: Robert Hussein, Jonathan Schmidt, Tomás Barros, Miguel A. L. Marques, Silvana Botti

Abstract: Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our dataset is constitute… ▽ More Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our dataset is constituted by experimental crystal structures of the Inorganic Crystal Structure Database matched with PBE-optmized structures stored in the materials project database. We complement these data with PBEsol calculations. We demonstrate that the accuracy and precision of PBE/PBEsol volume predictions can be noticeably improved a posteriori by employing simple, explainable machine learning models. These models can improve PBE unit cell volumes to match the accuracy of PBEsol calculations, and reduce the error of the latter with respect to experiment by 35%. Further, the error of PBE lattice constants is reduced by a factor of 3--5. A further benefit of our approach is the implicit correction of finite temperature effects without performing phonon calculations. △ Less

Submitted 3 November, 2021; originally announced November 2021.

Comments: 62 pages, 5 figures, 3 tables

Journal ref: MRS Bulletin (2022)

Machine learning the derivative discontinuity of density-functional theory

Authors: Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson, Jack Wetherell, Carlos L. Benavides-Riveros, Miguel A. L. Marques

Abstract: Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number o… ▽ More Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (i) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (ii) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation. △ Less

Submitted 30 June, 2021; originally announced June 2021.

Journal ref: Mach. Learn.: Sci. Technol. 3 015011 (2022)

Bandgap of two-dimensional materials: Thorough assessment of modern exchange-correlation functionals

Authors: Fabien Tran, Jan Doumont, Leila Kalantari, Peter Blaha, Tomáš Rauch, Pedro Borlido, Silvana Botti, Miguel A. L. Marques, Abhilash Patra, Subrata Jana, Prasanjit Samal

Abstract: The density functional theory (DFT) approximations that are the most accurate for the calculation of band gap of bulk materials are hybrid functionals like HSE06, the MBJ potential, and the GLLB-SC potential. More recently, generalized gradient approximations (GGA), like HLE16, or meta-GGAs, like (m)TASK, have proven to be also quite accurate for the band gap. Here, the focus is on 2D materials an… ▽ More The density functional theory (DFT) approximations that are the most accurate for the calculation of band gap of bulk materials are hybrid functionals like HSE06, the MBJ potential, and the GLLB-SC potential. More recently, generalized gradient approximations (GGA), like HLE16, or meta-GGAs, like (m)TASK, have proven to be also quite accurate for the band gap. Here, the focus is on 2D materials and the goal is to provide a broad overview of the performance of DFT functionals by considering a large test set of 298 2D systems. The present work is an extension of our recent studies [Rauch et al., Phys. Rev. B 101, 245163 (2020) and Patra et al., J. Phys. Chem. C 125, 11206 (2021)]. Due to the lack of experimental results for the band gap of 2D systems, $G_{0}W_{0}$ results were taken as reference. It is shown that the GLLB-SC potential and mTASK functional provide the band gaps that are the closest to $G_{0}W_{0}$. Following closely, the local MBJ potential has a pretty good accuracy that is similar to the accuracy of the more expensive hybrid functional HSE06. △ Less

Submitted 3 January, 2022; v1 submitted 25 May, 2021; originally announced May 2021.

Journal ref: J. Chem. Phys. 155, 104103 (2021)

Stable ordered phases of cuprous iodide with complexes of copper vacancies

Authors: Stefan Jaschik, Mário R. G. Marques, Michael Seifert, Claudia Rödl, Silvana Botti, Miguel A. L. Marques

Abstract: We perform an exhaustive theoretical study of the phase diagram of Cu-I binaries, focusing on Cu-poor compositions, relevant for p-type transparent conduction. We find that the interaction between neighboring Cu vacancies is the determining factor that stabilizes non-stoichiometric zincblende phases. This interaction leads to defect complexes where Cu vacancies align preferentially along the [100]… ▽ More We perform an exhaustive theoretical study of the phase diagram of Cu-I binaries, focusing on Cu-poor compositions, relevant for p-type transparent conduction. We find that the interaction between neighboring Cu vacancies is the determining factor that stabilizes non-stoichiometric zincblende phases. This interaction leads to defect complexes where Cu vacancies align preferentially along the [100] crystallographic direction. It turns out that these defect complexes have an important influence on hole conductivity, as they lead to dispersive conducting $p$-states that extend up to around 0.8 eV above the Fermi level. We furthermore observe a characteristic peak in the density of electronic states, which could provide an experimental signature for this type of defect complexes. △ Less

Submitted 14 September, 2020; originally announced September 2020.

Journal ref: Chem. Mater. 31, 7877-7882 (2019)

First-principles identification of single photon emitters based on carbon clusters in hexagonal boron nitride

Authors: Cesar Jara, T. Rauch, Silvana Botti, Miguel A. L. Marques, A. Norambuena, R. Coto, J. R. Maze, F. Munoz

Abstract: A recent study associate carbon with single photon emitters (SPEs) in hexagonal boron nitride (h-BN). This observation, together with the high mobility of carbon in h-BN suggest the existence of SPEs based on carbon clusters. Here, by means of density-functional theory calculations we studied clusters of substitutional carbon atoms up to tetramers in hexagonal boron nitride. Two different conforma… ▽ More A recent study associate carbon with single photon emitters (SPEs) in hexagonal boron nitride (h-BN). This observation, together with the high mobility of carbon in h-BN suggest the existence of SPEs based on carbon clusters. Here, by means of density-functional theory calculations we studied clusters of substitutional carbon atoms up to tetramers in hexagonal boron nitride. Two different conformations of neutral carbon trimers have zero-point line energies and shifts of the phonon sideband compatible with typical photoluminescence spectra. Moreover, some conformations of two small C clusters next to each other result in photoluminescence spectra similar to those found in experiments. We also showed that vacancies are unable to reproduce the typical features of the phonon sideband observed in most measurements due to the large spectral weight of low-energy breathing modes, ubiquitous in such defects. △ Less

Submitted 31 July, 2020; originally announced July 2020.

Meta-local density functionals: a new rung on Jacob's ladder

Authors: Susi Lehtola, Miguel A. L. Marques

Abstract: The homogeneous electron gas (HEG) is a key ingredient in the construction of most exchange-correlation functionals of density-functional theory. Often, the energy of the HEG is parameterized as a function of its spin density $n$, leading to the local density approximation (LDA) for inhomogeneous systems. However, the connection between the electron density and kinetic energy density of the HEG ca… ▽ More The homogeneous electron gas (HEG) is a key ingredient in the construction of most exchange-correlation functionals of density-functional theory. Often, the energy of the HEG is parameterized as a function of its spin density $n$, leading to the local density approximation (LDA) for inhomogeneous systems. However, the connection between the electron density and kinetic energy density of the HEG can be used to generalize the LDA by evaluating it on a weighted geometric average of the local spin density and the spin density of a HEG that has the local kinetic energy density of the inhomogeneous system, with a mixing ratio $x$. This leads to a new family of functionals that we term meta-local density approximations (meta-LDAs), which are still exact for the HEG, which are derived only from properties of the HEG, and which form a new rung of Jacob's ladder of density functionals. The first functional of this ladder, the local $τ$ approximation (LTA) of Ernzerhof and Scuseria that corresponds to $x=1$ is unfortunately not stable enough to be used in self-consistent field calculations, because it leads to divergent potentials as we show in this work. However, a geometric averaging of the LDA and LTA densities with smaller values of $x$ not only leads to numerical stability of the resulting functional, but also yields more accurate exchange energies in atomic calculations than the LDA, the LTA, or the tLDA functional ($x=1/4$) of Eich and Hellgren. We choose $x=0.50$ as it gives the best total energy in self-consistent exchange-only calculations for the argon atom. Atomization energy benchmarks confirm that the choice $x=0.50$ also yields improved energetics in combination with correlation functionals in molecules, almost eliminating the well-known overbinding of the LDA and reducing its error by two thirds. △ Less

Submitted 14 January, 2021; v1 submitted 30 June, 2020; originally announced June 2020.

Comments: 9 pages, 5 figures including supporting information

Journal ref: J. Chem. Theory Comput. 17, 943 (2021)

The CECAM Electronic Structure Library and the modular software development paradigm

Authors: Micael J. T. Oliveira, Nick Papior, Yann Pouillon, Volker Blum, Emilio Artacho, Damien Caliste, Fabiano Corsetti, Stefano de Gironcoli, Alin M. Elena, Alberto Garcia, Victor M. Garcia-Suarez, Luigi Genovese, William P. Huhn, Georg Huhs, Sebastian Kokott, Emine Kucukbenli, Ask H. Larsen, Alfio Lazzaro, Irina V. Lebedeva, Yingzhou Li, David Lopez-Duran, Pablo Lopez-Tarifa, Martin Luders, Miguel A. L. Marques, Jan Minar , et al. (12 additional authors not shown)

Abstract: First-principles electronic structure calculations are very widely used thanks to the many successful software packages available. Their traditional coding paradigm is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, from the compiler up, with the exception of linear-algebra and message-passing libraries. This model has been qu… ▽ More First-principles electronic structure calculations are very widely used thanks to the many successful software packages available. Their traditional coding paradigm is monolithic, i.e., regardless of how modular its internal structure may be, the code is built independently from others, from the compiler up, with the exception of linear-algebra and message-passing libraries. This model has been quite successful for decades. The rapid progress in methodology, however, has resulted in an ever increasing complexity of those programs, which implies a growing amount of replication in coding and in the recurrent re-engineering needed to adapt to evolving hardware architecture. The Electronic Structure Library (\esl) was initiated by CECAM (European Centre for Atomic and Molecular Calculations) to catalyze a paradigm shift away from the monolithic model and promote modularization, with the ambition to extract common tasks from electronic structure programs and redesign them as free, open-source libraries. They include "heavy-duty" ones with a high degree of parallelisation, and potential for adaptation to novel hardware within them, thereby separating the sophisticated computer science aspects of performance optimization and re-engineering from the computational science done by scientists when implementing new ideas. It is a community effort, undertaken by developers of various successful codes, now facing the challenges arising in the new model. This modular paradigm will improve overall coding efficiency and enable specialists (computer scientists or computational scientists) to use their skills more effectively. It will lead to a more sustainable and dynamic evolution of software as well as lower barriers to entry for new developers. △ Less

Submitted 24 June, 2020; v1 submitted 11 May, 2020; originally announced May 2020.

Comments: Revised version as finally accepted by J. Chem. Phys. to appear within the Special Topic in Electronic Structure Software (version prior to JCP's typesetting and proofs)

Homogeneous electron gas in arbitrary dimensions

Authors: Robert Schlesier, Carlos L. Benavides-Riveros, Miguel A. L. Marques

Abstract: The homogeneous electron gas is one of the most studied model systems in condensed matter physics. It is also at the basis of the large majority of approximations to the functionals of density functional theory. As such, its exchange-correlation energy has been extensively studied, and is well-known for systems of 1, 2, and 3 dimensions. Here, we extend this model and compute the exchange and corr… ▽ More The homogeneous electron gas is one of the most studied model systems in condensed matter physics. It is also at the basis of the large majority of approximations to the functionals of density functional theory. As such, its exchange-correlation energy has been extensively studied, and is well-known for systems of 1, 2, and 3 dimensions. Here, we extend this model and compute the exchange and correlation energy, as a function of the Wigner-Seitz radius $r_s$, for arbitrary dimension $D$. We find a very different behavior for reduced dimensional spaces ($D=1$ and 2), our three dimensional space, and for higher dimensions. In fact, for $D > 3$, the leading term of the correlation energy does not depend on the logarithm of $r_s$ (as for $D=3$), but instead scales polynomialy: $ -c_D /r_s^{γ_D}$, with the exponent $γ_D=(D-3)/(D-1)$. In the large-$D$ limit, the value of $c_D$ is found to depend linearly with the dimension. In this limit, we also find that the concepts of exchange and correlation merge, sharing a common $1/r_s$ dependence. △ Less

Submitted 26 June, 2020; v1 submitted 11 May, 2020; originally announced May 2020.

Comments: 9 pages, 2 figure, minor changes

Journal ref: Phys. Rev. B 102, 035123 (2020)

Accurate electronic band gaps of two-dimensional materials from the local modified Becke-Johnson potential

Authors: Tomáš Rauch, Miguel A. L. Marques, Silvana Botti

Abstract: The electronic band structures of two-dimensional materials are significantly different from those of their bulk counterparts, due to quantum confinement and strong modifications of electronic screening. An accurate determination of electronic states is a prerequisite to design electronic or optoelectronic applications of two-dimensional materials, however, most of the theoretical methods we have… ▽ More The electronic band structures of two-dimensional materials are significantly different from those of their bulk counterparts, due to quantum confinement and strong modifications of electronic screening. An accurate determination of electronic states is a prerequisite to design electronic or optoelectronic applications of two-dimensional materials, however, most of the theoretical methods we have available to compute band gaps are either inaccurate, computationally expensive, or only applicable to bulk systems. Here we show that reliable band structures of nanostructured systems can now be efficiently calculated using density-functional theory with the local modified Becke-Johnson exchange-correlation functional that we recently proposed. After re-optimizing the parameters of this functional specifically for two-dimensional materials, we show, for a test set of almost 300 systems, that the obtained band gaps are of comparable quality as those obtained using the best hybrid functionals, but at a very reduced computational cost. These results open the way for accurate high-throughput studies of band-structures of two-dimensional materials and for the study of van der Waals heterostructures with large unit cells. △ Less

Submitted 16 April, 2020; originally announced April 2020.

Journal ref: Phys. Rev. B 101, 245163 (2020)

Iodine molecule modifications with high pressure

Authors: Jingming Shi, Emiliano Fonda, Silvana Botti, Miguel A. L. Marques, Toru Shinmei, Tetsuo Irifune, Anne-Marie Flank, Pierre Lagarde, Alain Polian, Jean-Paul Itié, Alfonso San-Miguel

Abstract: Metallization and dissociation are key transformations in diatomic molecules at high densities particularly significant for modeling giant planets. Using X-ray absorption spectroscopy and atomistic modeling, we demonstrate that in halogens, the formation of a \textit{connected} molecular structure takes place at pressures well below metallization. Here we show that the iodine diatomic molecule fir… ▽ More Metallization and dissociation are key transformations in diatomic molecules at high densities particularly significant for modeling giant planets. Using X-ray absorption spectroscopy and atomistic modeling, we demonstrate that in halogens, the formation of a \textit{connected} molecular structure takes place at pressures well below metallization. Here we show that the iodine diatomic molecule first elongates of $\sim$0.007 Å~up to a critical pressure of $P_c$ $\backsim$7~GPa developing bonds between molecules. Then its length continuously decreases with pressure up to 15-20~GPa. Universal trends in halogens are shown and allow to predict for chlorine a pressure of 42$\pm$8~GPa for molecular bond-length reversal. Our findings tackle the molecule invariability paradigm in diatomic molecular phases at high pressures and may be generalized to other abundant diatomic molecules in the universe, including hydrogen. △ Less

Submitted 11 September, 2020; v1 submitted 16 April, 2020; originally announced April 2020.

Comments: 6 pages, 4 figures

Reduced Density Matrix Functional Theory for Bosons

Authors: Carlos L. Benavides-Riveros, Jakob Wolff, Miguel A. L. Marques, Christian Schilling

Abstract: Based on a generalization of Hohenberg-Kohn's theorem, we propose a ground state theory for bosonic quantum systems. Since it involves the one-particle reduced density matrix $γ$ as a natural variable but still recovers quantum correlations in an exact way it is particularly well-suited for the accurate description of Bose-Einstein condensates. As a proof of principle we study the building block o… ▽ More Based on a generalization of Hohenberg-Kohn's theorem, we propose a ground state theory for bosonic quantum systems. Since it involves the one-particle reduced density matrix $γ$ as a natural variable but still recovers quantum correlations in an exact way it is particularly well-suited for the accurate description of Bose-Einstein condensates. As a proof of principle we study the building block of optical lattices. The solution of the underlying $v$-representability problem is found and its peculiar form identifies the constrained search formalism as the ideal starting point for constructing accurate functional approximations: The exact functionals for this $N$-boson Hubbard dimer and general Bogoliubov-approximated systems are determined. The respective gradient forces are found to diverge in the regime of Bose-Einstein condensation, $\nabla_γ \mathcal{F} \propto 1/\sqrt{1-N_{\mathrm{BEC}}/N}$, providing a natural explanation for the absence of complete BEC in nature. △ Less

Submitted 17 February, 2020; originally announced February 2020.

Journal ref: Phys. Rev. Lett. 124, 180603 (2020)

Octopus, a computational framework for exploring light-driven phenomena and quantum dynamics in extended and finite systems

Authors: Nicolas Tancogne-Dejean, Micael J. T. Oliveira, Xavier Andrade, Heiko Appel, Carlos H. Borca, Guillaume Le Breton, Florian Buchholz, Alberto Castro, Stefano Corni, Alfredo A. Correa, Umberto De Giovannini, Alain Delgado, Florian G. Eich, Johannes Flick, Gabriel Gil, Adrián Gomez, Nicole Helbig, Hannes Hübener, René Jestädt, Joaquim Jornet-Somoza, Ask H. Larsen, Irina V. Lebedeva, Martin Lüders, Miguel A. L. Marques, Sebastian T. Ohlmann , et al. (9 additional authors not shown)

Abstract: Over the last years extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high-degree of precision. An appealing and challenging route towards engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a… ▽ More Over the last years extraordinary advances in experimental and theoretical tools have allowed us to monitor and control matter at short time and atomic scales with a high-degree of precision. An appealing and challenging route towards engineering materials with tailored properties is to find ways to design or selectively manipulate materials, especially at the quantum level. To this end, having a state-of-the-art ab initio computer simulation tool that enables a reliable and accurate simulation of light-induced changes in the physical and chemical properties of complex systems is of utmost importance. The first principles real-space-based Octopus project was born with that idea in mind, providing an unique framework allowing to describe non-equilibrium phenomena in molecular complexes, low dimensional materials, and extended systems by accounting for electronic, ionic, and photon quantum mechanical effects within a generalized time-dependent density functional theory framework. The present article aims to present the new features that have been implemented over the last few years, including technical developments related to performance and massive parallelism. We also describe the major theoretical developments to address ultrafast light-driven processes, like the new theoretical framework of quantum electrodynamics density-functional formalism (QEDFT) for the description of novel light-matter hybrid states. Those advances, and other being released soon as part of the Octopus package, will enable the scientific community to simulate and characterize spatial and time-resolved spectroscopies, ultrafast phenomena in molecules and materials, and new emergent states of matter (QED-materials). △ Less

Submitted 17 December, 2019; originally announced December 2019.

Journal ref: J. Chem. Phys. 152, 124119 (2020)

Local modified Becke-Johnson exchange-correlation potential for interfaces, surfaces, and two-dimensional materials

Authors: Tomáš Rauch, Miguel A. L. Marques, Silvana Botti

Abstract: The modified Becke-Johnson meta-GGA potential of density functional theory has been shown to be the best exchange-correlation potential to determine band gaps of crystalline solids. However, it cannot be consistently used for the electronic structure of non-periodic or nanostructured systems. We propose an extension of this potential that enables its use to study heterogeneous, finite and low-dime… ▽ More The modified Becke-Johnson meta-GGA potential of density functional theory has been shown to be the best exchange-correlation potential to determine band gaps of crystalline solids. However, it cannot be consistently used for the electronic structure of non-periodic or nanostructured systems. We propose an extension of this potential that enables its use to study heterogeneous, finite and low-dimensional systems. This is achieved by using a coordinate-dependent expression for the parameter $c$ that weights the Becke-Russel exchange, in contrast to the original global formulation, where $c$ is just a fitted number. Our potential takes advantage of the excellent description of band gaps provided by the modified Becke-Johnson potential and preserves its modest computational effort. Furthermore, it yields with one single calculation band diagrams and band offsets of heterostructures and surfaces. We exemplify the usefulness and efficiency of our local meta-GGA potential by testing it for a series of interfaces (Si/SiO$_2$, AlAs/GaAs, AlP/GaP, and GaP/Si), a Si surface, and boron nitride monolayer. △ Less

Submitted 24 February, 2020; v1 submitted 1 November, 2019; originally announced November 2019.