Showing 1–50 of 85 results for author: Rinke, P

Charting the thermodynamic stability of hybrid perovskite alloys with machine learning

Authors: Jarno Laakso, Armi Tiihonen, Patrick Rinke

Abstract: Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for the phase stability of (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 perovskites, with FA being formamidinium. To make such quaternary alloys tractable, we adopt a two-le… ▽ More Alloy-based perovskite solar cells offer tunable properties and improved stability, but their complexity has impeded accurate modeling, hindering development. We present a machine-learning (ML) accelerated atomistic modeling approach for the phase stability of (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 perovskites, with FA being formamidinium. To make such quaternary alloys tractable, we adopt a two-level ML strategy, combining 1) graph neural network interatomic potentials trained on density functional theory data for efficient structure relaxations with 2) secondary ML models for direct energy prediction from unrelaxed structures. These models enable computations of free energy landscapes across compositions and phases, capturing alloy disorder and FA molecular orientations. Our results reveal narrower stable composition regions for the Sn-based system compared to its Pb-based counterpart, limiting options for compositional engineering. Maximum stability occurs at high I content, and no stabilization is observed near the center of the composition space. Our results guide the design of stable perovskites. △ Less

Submitted 28 May, 2026; originally announced May 2026.

Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields

Authors: Prajwal Pisal, Ondřej Krejčí, Patrick Rinke

Abstract: Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AEDs more fully represent nanoparticle-based catalytic surfaces and show strong promise for acceleratin… ▽ More Adsorption energy distributions (AEDs) have emerged as a powerful and increasingly adopted descriptor for catalytic performance in high-entropy alloys and, more recently, in conventional metallic alloy nanocrystal catalysts. By accounting for diverse adsorption sites and crystallographic facets, AEDs more fully represent nanoparticle-based catalytic surfaces and show strong promise for accelerating rational design and discovery of heterogeneous catalysts, especially for CO$_2$ hydrogenation. However, previous approaches have not sufficiently resolved facet-specific contributions, despite the catalytic significance and prevalence of certain Miller planes in nanoscale catalysts, limiting their applicability in predicting activity and selectivity. Here, we introduce an updated facet-resolved framework for predicting catalytic activity, which also enables insight into selectivity toward C1 products. Universal machine-learned force fields trained on Open Catalyst Project data were employed to compute adsorption energetics across 226 experimentally observed metals, binary alloys, and ternary alloys, encompassing 1.4 million adsorption sites on 2,626 crystallographically distinct surfaces. Using statistical and unsupervised learning techniques, we analyzed facet-specific AEDs to identify highly active and methanol-selective facets. Our approach provides insight into the relationship between structure and catalytic performance metrics like activity and selectivity, and presents a set of alloy compositions and their respective surface orientations for experimental validation toward highly selective CO$_2$ hydrogenation. △ Less

Submitted 8 May, 2026; originally announced May 2026.

Comments: 30 pages, 5 figures + 1 toc, 2 tables, Supplementary Information

Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences

Authors: Yuhao Zhang, Ti John, Matthias Stosiek, Patrick Rinke

Abstract: Optimizing expensive black-box objectives over mixed search spaces is a common challenge across the natural sciences. Bayesian optimization (BO) offers sample-efficient strategies through probabilistic surrogate models and acquisition functions. However, its effectiveness diminishes in mixed or high-cardinality discrete spaces, where gradients are unavailable and optimizing the acquisition functio… ▽ More Optimizing expensive black-box objectives over mixed search spaces is a common challenge across the natural sciences. Bayesian optimization (BO) offers sample-efficient strategies through probabilistic surrogate models and acquisition functions. However, its effectiveness diminishes in mixed or high-cardinality discrete spaces, where gradients are unavailable and optimizing the acquisition function becomes computationally demanding. In this work, we generalize the probabilistic reparameterization (PR) approach of Daulton et al. to handle non-equidistant discrete variables, enabling gradient-based optimization in fully mixed-variable settings with Gaussian process (GP) surrogates. With real-world scientific optimization tasks in mind, we conduct systematic benchmarks on synthetic and experimental objectives to obtain an optimized kernel formulations and demonstrate the robustness of our generalized PR method. We additionally show that, when combined with a modified BO workflow, our approach can efficiently optimize highly discontinuous and discretized objective landscapes. This work establishes a practical BO framework for addressing fully mixed optimization problems in the natural sciences, and is particularly well suited to autonomous laboratory settings where noise, discretization, and limited data are inherent. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Role of photonic interference in exciton-mediated magneto-optic responses

Authors: Güven Budak, Christian Riedel, Akashdeep Kamra, Patrick Rinke, Christian Back, Matthias Stosiek, Florian Dirnberger

Abstract: Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excitons and magnons emerge from the same electronic orbitals. While previous work has primarily focused on uncovering the magneto-electric origin of the resulting exciton-magnon interactions, the influe… ▽ More Coupled optical and magnetic excitations can give rise to remarkably strong magneto-optic responses. This is particularly evident in van der Waals magnets, such as the antiferromagnet CrSBr, where excitons and magnons emerge from the same electronic orbitals. While previous work has primarily focused on uncovering the magneto-electric origin of the resulting exciton-magnon interactions, the influence of photonic effects has received comparatively little attention. Here, we use numerical simulations to disentangle exciton-magnon coupling from the exciton-mediated magnon-photon interactions observed in optical experiments. Our simulations show the strong dependence of these interactions on photonic interference and dispersion effects near excitonic resonances. Such effects shape the optical response to coherent magnons and make it intrinsically non-linear in the magnon-induced exciton energy shift. Thermal magnons, which have a particularly pronounced impact on excitons, are found to even produce qualitatively different trends in optical signatures. Depending on weak or strong coupling of excitons and photons, the same exciton-magnon interaction can lead to a red-shift of optical modes, a nearly vanishing response, or their blue-shift. Finally, we demonstrate first steps towards optimizing the multi-parameter problem of efficient magnon-photon transduction using a machine-learning approach. △ Less

Submitted 9 March, 2026; originally announced March 2026.

Journal ref: Phys. Rev. B 114, 094405 (2026)

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks

Authors: Xiangzhou Zhu, Patrick Rinke, David A. Egger

Abstract: The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to inv… ▽ More The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials. △ Less

Submitted 23 November, 2025; originally announced November 2025.

Efficient dataset generation for machine learning perovskite alloys

Authors: Henrietta Homm, Jarno Laakso, Patrick Rinke

Abstract: Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can acc… ▽ More Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can accelerate property predictions for the CsPb(Cl/Br)$_3$ perovskite alloy. However, the substantial computational demand of density functional theory (DFT) calculations required for model training prevents applications to more complex materials. Here, we introduce a data-efficient scheme to facilitate model training, validated initially on CsPb(Cl/Br)$_3$ data and extended to the ternary alloy CsSn(Cl/Br/I)$_3$. Our approach employs clustering to construct a compact yet diverse initial dataset of atomic structures. We then apply a two-stage active learning approach to first improve the reliability of the ML-based structure relaxations and then refine accuracy near equilibrium structures. Tests for CsPb(Cl/Br)$_3$ demonstrate that our scheme reduces the number of required DFT calculations during the different parts of our proposed model training method by up to 20% and 50%. The fitted model for CsSn(Cl/Br/I)$_3$ is robust and highly accurate, evidenced by the convergence of all ML-based structure relaxations in our tests and an average relaxation error of only 0.5 meV/atom. △ Less

Submitted 6 June, 2025; originally announced June 2025.

Comments: Main text 11 pages, 7 figures, with supplementary material 6 pages, 5 figures

Journal ref: Physical Review Materials, 9(5), 053802 (2025)

Design Rules for Optimizing Quaternary Mixed-Metal Chalcohalides

Authors: Pascal Henkel, Jingrui Li, Patrick Rinke

Abstract: Quaternary mixed-metal M(II)2M(III)Ch2X3 chalcohalides are an emerging material class for photovoltaic absorbers that combines the beneficial optoelectronic properties of lead-based halide perovskites with the stability of metal chalcogenides. Inspired by the recent discovery of lead-free mixed-metal chalcohalides materials, we utilized a combination of density functional theory and machine learni… ▽ More Quaternary mixed-metal M(II)2M(III)Ch2X3 chalcohalides are an emerging material class for photovoltaic absorbers that combines the beneficial optoelectronic properties of lead-based halide perovskites with the stability of metal chalcogenides. Inspired by the recent discovery of lead-free mixed-metal chalcohalides materials, we utilized a combination of density functional theory and machine learning to determine compositional trends and chemical design rules in the lead-free and lead-based materials spaces. We explored a total of 54 M(II)2M(III)Ch2X3 materials with M(II) = Sn, Pb, M(III) = In, Sb, Bi, Ch = S, Se, Te, and X = Cl, Br, I per phase (Cmcm, Cmc21 , and P21/c). The P21/c phase is the equilibrium phase at low temperatures, followed by Cmc21 and Cmcm. The fundamental band gaps in Cmcm and Cmc21 are smaller than those in P21/c, but direct band gaps are more common in Cmcm and Cmc21. The effective electron masses in P21/c are significantly larger compared to Cmcm and Cmc21, while the effective hole masses are nearly the same across all three phases. Using random forest regression, we found that the two electron acceptor sites (Ch and X) are crucial in shaping the properties of mixed-metal chalcohalide compounds. Furthermore, the electron donor sites (M(II) and M(III)) can be used to finetune the material properties to desired applications. These design rules enable precise tailoring of mixed-metal chalcohalide compounds for a variety of applications. △ Less

Submitted 19 November, 2025; v1 submitted 26 May, 2025; originally announced May 2025.

Journal ref: Phys. Rev. Materials 9, 115405 (2025)

Roadmap on Advancements of the FHI-aims Software Package

Authors: Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush, Alberto Ambrosetti, Viktor Atalla, Alexej Bagrets, Jörg Behler, Daniel Berger, Hannah Bertschi, Björn Bieniek, Jonas Björk, Volker Blum, Saeed Bohloul, Connor L. Box, Nicholas Boyer, Danilo Simoes Brambila, Gabriel A. Bramley, Kyle R. Bryenton, María Camarasa-Gómez, Christian Carbogno, Fabio Caruso, Sucismita Chutia, Michele Ceriotti, Gábor Csányi, William Dawson , et al. (181 additional authors not shown)

Abstract: Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precis… ▽ More Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accuracy at the base, reliable predictions are unlikely at any level that follows. The software package FHI-aims has proven to be a game changer for accurate free-energy calculations because of its scalability, numerical precision, and its efficient handling of density functional theory (DFT) with hybrid functionals and van der Waals interactions. It treats molecules, clusters, and extended systems (solids and liquids) on an equal footing. Besides DFT, FHI-aims also includes quantum-chemistry methods, descriptions for excited states and vibrations, and calculations of various types of transport. Recent advancements address the integration of FHI-aims into an increasing number of workflows and various artificial intelligence (AI) methods. This Roadmap describes the state-of-the-art of FHI-aims and advancements that are currently ongoing or planned. △ Less

Submitted 20 April, 2026; v1 submitted 30 April, 2025; originally announced May 2025.

Comments: arXiv admin note: Includes articles arXiv:2502.02460, arXiv:2501.02550, arXiv:2411.01680, arXiv:2501.16091, arXiv:2411.04951

Exploring Noncollinear Magnetic Energy Landscapes with Bayesian Optimization

Authors: Jakob Baumsteiger, Lorenzo Celiberti, Patrick Rinke, Milica Todorović, Cesare Franchini

Abstract: The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging task. Complex interactions, such as superexchange and spin-orbit coupling, make these calculations computationally expensive and often lead to non-trivial energy landscapes. Consequently, a comprehensive and systematic… ▽ More The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging task. Complex interactions, such as superexchange and spin-orbit coupling, make these calculations computationally expensive and often lead to non-trivial energy landscapes. Consequently, a comprehensive and systematic investigation of large magnetic configuration spaces is often impractical. We approach this problem by utilizing Bayesian Optimization, an active machine learning scheme that has proven to be efficient in modeling unknown functions and finding global minima. Using this approach we can obtain the magnetic contribution to the energy as a function of one or more spin canting angles with relatively small numbers of DFT calculations. To assess the capabilities and the efficiency of the approach we investigate the noncollinear magnetic energy landscapes of selected materials containing 3d, 5d and 5f magnetic ions: Ba$_3$MnNb$_2$O$_9$, LaMn$_2$Si$_2$, $β$-MnO$_2$, Sr$_2$IrO$_4$, UO$_2$ and Ba$_2$NaOsO$_6$. By comparing our results to previous ab initio studies that followed more conventional approaches, we observe significant improvements in efficiency. △ Less

Submitted 20 December, 2024; originally announced December 2024.

Comments: 10 pages, 3 figures

Machine Learning Accelerated Descriptor Design for Catalyst Discovery in CO$_2$ to Methanol Conversion

Authors: Prajwal Pisal, Ondrej Krejci, Patrick Rinke

Abstract: Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts, us… ▽ More Transforming CO$_2$ into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. We propose new promising candidates such as ZnRh and ZnPt$_3$, which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability. △ Less

Submitted 4 July, 2025; v1 submitted 18 December, 2024; originally announced December 2024.

Comments: 24 pages, 4 figures + 6 pages, 1 figure (supplementary). Revised version: Updated the journal reference

Journal ref: npj Comput Mater 11, 213 (2025)

Precision benchmarks for solids: G0W0 calculations with different basis sets

Authors: Maryam Azizi, Francisco A. Delesma, Matteo Giantomassi, Davis Zavickis, Mikael Kuisma, Kristian Thyghesen, Dorothea Golze, Alexander Buccheri, Min-Ye Zhang, Patrick Rinke, Claudia Draxl, Andris Gulans, Xavier Gonze

Abstract: The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained from a Density Functional Theory (DFT) calculation are used as a starting point to build the Green's function G and the screened Coulomb interaction W, yielding the one-shot G0W0 selfenergy if no further… ▽ More The GW approximation within many-body perturbation theory is the state of the art for computing quasiparticle energies in solids. Typically, Kohn-Sham (KS) eigenvalues and eigenfunctions, obtained from a Density Functional Theory (DFT) calculation are used as a starting point to build the Green's function G and the screened Coulomb interaction W, yielding the one-shot G0W0 selfenergy if no further update of these quantities are made. Multiple implementations exist for both the DFT and the subsequent G0W0 calculation, leading to possible differences in quasiparticle energies. In the present work, the G0W0 quasiparticle energies for states close to the band gap are calculated for six crystalline solids, using four different codes: Abinit, exciting, FHI-aims, and GPAW. This comparison helps to assess the impact of basis-set types (planewaves versus localized orbitals) and the treatment of core and valence electrons (all-electron full potentials versus pseudopotentials). The impact of unoccupied states as well as the algorithms for solving the quasiparticle equation are also briefly discussed. For the KS-DFT band gaps, we observe good agreement between all codes, with differences not exceeding 0.1 eV, while the G0W0 results deviate on the order of 0.1-0.3 eV. Between all-electron codes (FHI-aims and exciting), the agreement is better than 15 meV for KS-DFT and, with one exception, about 0.1 eV for G0W0 band gaps. △ Less

Submitted 29 November, 2024; originally announced November 2024.

Comments: 14 pages, 2 figures

ACM Class: G.1.4

Validation of the GreenX library time-frequency component for efficient GW and RPA calculations

Authors: Maryam Azizi, Jan Wilhelm, Dorothea Golze, Francisco A. Delesma, Ramón L. Panadés-Barrueta, Patrick Rinke, Matteo Giantomassi, Xavier Gonze

Abstract: Electronic structure calculations based on many-body perturbation theory (e.g. GW or the random-phase approximation (RPA)) require function evaluations in the complex time and frequency domain, for example inhomogeneous Fourier transforms or analytic continuation from the imaginary axis to the real axis. For inhomogeneous Fourier transforms, the time-frequency component of the GreenX library provi… ▽ More Electronic structure calculations based on many-body perturbation theory (e.g. GW or the random-phase approximation (RPA)) require function evaluations in the complex time and frequency domain, for example inhomogeneous Fourier transforms or analytic continuation from the imaginary axis to the real axis. For inhomogeneous Fourier transforms, the time-frequency component of the GreenX library provides time-frequency grids that can be utilized in low-scaling RPA and GW implementations. In addition, the adoption of the compact frequency grids provided by our library also reduces the computational overhead in RPA implementations with conventional scaling. In this work, we present low-scaling GW and conventional RPA benchmark calculations using the GreenX grids with different codes (FHI-aims, CP2K and ABINIT) for molecules, two-dimensional materials and solids. Very small integration errors are observed when using 30 time-frequency points for our test cases, namely $<10^{-8}$ eV/electron for the RPA correlation energies, and 10 meV for the GW quasiparticle energies. △ Less

Submitted 12 March, 2024; v1 submitted 11 March, 2024; originally announced March 2024.

Comments: 15 pages, 6 figures

Screening Mixed-Metal Sn$_2$M(III)Ch$_2$X$_3$ Chalcohalides for Photovoltaic Applications

Authors: Pascal Henkel, Jingrui Li, G. Krishnamurthy Grandhi, Paola Vivo, Patrick Rinke

Abstract: Quaternary mixed-metal chalcohalides (Sn$_2$BCh$_2$X$_3$) are emerging as promising lead-free perovskite-inspired photovoltaic absorbers. Motivated by recent developments of a first Sn$_2$BCh$_2$X$_3$-based device, we used density functional theory to identify lead-free Sn$_2$BCh$_2$X$_3$ materials that are structurally and energetically stable within Cmcm, Cmc2$_1$ and P2$_1$/c space groups and h… ▽ More Quaternary mixed-metal chalcohalides (Sn$_2$BCh$_2$X$_3$) are emerging as promising lead-free perovskite-inspired photovoltaic absorbers. Motivated by recent developments of a first Sn$_2$BCh$_2$X$_3$-based device, we used density functional theory to identify lead-free Sn$_2$BCh$_2$X$_3$ materials that are structurally and energetically stable within Cmcm, Cmc2$_1$ and P2$_1$/c space groups and have a band gap in the range of 0.7 to 2.0 eV to cover out- and indoor photovoltaic applications. A total of 27 Sn$_2$BCh$_2$X$_3$ materials were studied, including Sb, Bi, In for B-site, S, Se, Te for Ch-site and Cl, Br, I for X-site. We identified 12 materials with a direct band gap that meet our requirements, namely: Sn$_2$InS$_2$Br$_3$, Sn$_2$InS$_2$I$_3$, Sn$_2$InSe$_2$Cl$_3$, Sn$_2$InSe$_2$Br$_3$, Sn$_2$InTe$_2$Br$_3$, Sn$_2$InTe$_2$Cl$_3$, Sn$_2$SbS$_2$I$_3$, Sn$_2$SbSe$_2$Cl$_3$, Sn$_2$SbSe$_2$I$_3$, Sn$_2$SbTe$_2$Cl$_3$, Sn$_2$BiS$_2$I$_3$ and Sn$_2$BiTe$_2$Cl$_3$. A database scan reveals that 9 out of 12 are new compositions. For all 27 materials, P2$_1$/c is the thermodynamically preferred structure, followed by Cmc2$_1$. In Cmcm and Cmc2$_1$ mainly direct gaps occur, whereas mostly indirects in P2$_1$/c. To open up the possibility of band gap tuning in the future, we identified 12 promising Sn$_2$B$_{1-{a}}$B$'_{a}$Ch$_{2-{b}}$Ch$'_{b}$X$_{3-{c}}$X$_{c}$ alloys which fulfill our requirements and additional 69 materials by combining direct and indirect band gap compounds. △ Less

Submitted 12 September, 2023; v1 submitted 30 June, 2023; originally announced June 2023.

Updates to the DScribe Library: New Descriptors and Derivatives

Authors: Jarno Laakso, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka, Marc O. J. Jäger, Milica Todorović, Patrick Rinke

Abstract: We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DSribe. For the ma… ▽ More We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DSribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys. △ Less

Submitted 24 March, 2023; originally announced March 2023.

Comments: The following article has been submitted to The Journal of Chemical Physics. After it is published, it will be found at https://aip.scitation.org/toc/jcp/current

Structural disorder by octahedral tilting in inorganic halide perovskites: New insight with Bayesian optimization

Authors: Jingrui Li, Fang Pan, Guo-Xu Zhang, Zenghui Liu, Hua Dong, Dawei Wang, Zhuangde Jiang, Wei Ren, Zuo-Guang Ye, Milica Todorović, Patrick Rinke

Abstract: Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. In this work, structural disorder in the benchmark inorganic halide perovsk… ▽ More Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. In this work, structural disorder in the benchmark inorganic halide perovskites CsPbI$_3^{}$ and CsPbBr$_3^{}$ is computationally studied in terms of the three octahedral-tilting angles. The consequent variation in energetics and properties are described by three-dimensional potential-energy surfaces (PESs) and property landscapes, delivered by Bayesian Optimization Structure Search method with integrated density-functional-theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in three-dimensional searches demonstrates the power of active learning and strategic sampling with Bayesian optimization. Further analysis indicates that disorder grows with increasing temperature, and reveals that the materials band gap at finite temperatures is a statistical mean over disordered structures. △ Less

Submitted 15 March, 2023; originally announced March 2023.

Single-atom dopants in plasmonic nanocatalysts

Authors: Daniel Sorvisto, Patrick Rinke, Tuomas P. Rossi

Abstract: Bimetallic nanostructures combining plasmonic and catalytic metals are promising for tailoring and enhancing plasmonic hot-carrier generation utilized in plasmonic catalysis. In this work, we study the plasmonic hot-carrier generation in noble metal nanoparticles (Ag, Au, Cu) with single-atom dopants (Ag, Au, Cu, Pd, Pt) with first-principles time-dependent density-functional theory calculations.… ▽ More Bimetallic nanostructures combining plasmonic and catalytic metals are promising for tailoring and enhancing plasmonic hot-carrier generation utilized in plasmonic catalysis. In this work, we study the plasmonic hot-carrier generation in noble metal nanoparticles (Ag, Au, Cu) with single-atom dopants (Ag, Au, Cu, Pd, Pt) with first-principles time-dependent density-functional theory calculations. Our results show that the local hot-carrier generation at the dopant atom is significantly altered by the dopant element while the plasmonic response of the nanoparticle as a whole is not significantly affected. In particular, hot holes at the dopant atom originate from the discrete d-electron states of the dopant. The energies of these d-electron states, and hence those of the hot holes, depend on the dopant element, which opens up the possibility to tune hot-carrier generation with suitable dopants. △ Less

Submitted 19 January, 2023; originally announced January 2023.

Comments: 6 pages, 4 figures

Protective Coating Interfaces for perovskite Solar Cell Materials: A first Principles Study

Authors: Azimatu Fangnon, Marc Dvorak, Ville Havu, Milica Todorovic, Jingrui Li, Patrick Rinke

Abstract: The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO3, and ZrO2 for the CsPbI3perovskite. The optimal interface registries are identified with Bayesian optimization. We then use semi-local density-functional theory (DF… ▽ More The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO3, and ZrO2 for the CsPbI3perovskite. The optimal interface registries are identified with Bayesian optimization. We then use semi-local density-functional theory (DFT) to determine the atomic structure at the interfaces of each coating material with the clean CsI-terminated surface and three reconstructed surface models with added PbI2and CsI complexes. For the final structures, we explore the level alignment at the interface with hybrid DFT calculations. Our analysis of the level alignment at the coating-substrate interfaces reveals no detrimental mid-gap states, but substrate-dependent valence and conduction band offsets. While ZnO and SrZrO3act as insulators on CsPbI3, ZrO2 might be suitable as electron transport layer with the right interface engineering. △ Less

Submitted 21 February, 2022; originally announced February 2022.

Comments: 27 pages, 16 figures

Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and $\boldsymbol{GW}$

Authors: Dorothea Golze, Markus Hirvensalo, Patricia Hernández-León, Anja Aarva, Jarkko Etula, Toma Susi, Patrick Rinke, Tomi Laurila, Miguel A. Caro

Abstract: We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with $GW$ and uses kernel ridge regression for the ML predictions. We apply the new approach to materials and molecules containing ca… ▽ More We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with $GW$ and uses kernel ridge regression for the ML predictions. We apply the new approach to materials and molecules containing carbon, hydrogen and oxygen, and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server. △ Less

Submitted 13 July, 2022; v1 submitted 13 December, 2021; originally announced December 2021.

Journal ref: Chem. Mater. (2022)

Surface reconstruction of tetragonal methylammonium lead triiodide

Authors: Azimatu Seidu, Marc Dvorak, Jari Järvi, Patrick Rinke, Jingrui Li

Abstract: We present a detailed first-principles analysis of the (001) surface of methylammonium lead triiodide (MAPbI3). With density-functional theory we investigate the atomic and electronic structure of the tetragonal (I4cm) phase of MAPbI3. We analysed surfaces models with MAI- (MAI-T) and PbI2-terminations(PbI2-T). For both terminations, we studied the clean-surface and a series of surface reconstruct… ▽ More We present a detailed first-principles analysis of the (001) surface of methylammonium lead triiodide (MAPbI3). With density-functional theory we investigate the atomic and electronic structure of the tetragonal (I4cm) phase of MAPbI3. We analysed surfaces models with MAI- (MAI-T) and PbI2-terminations(PbI2-T). For both terminations, we studied the clean-surface and a series of surface reconstructions. We find that the clean MAI-T model is more stable than its PbI2-T counterpart. For the MAI termination,reconstructions with added or removed units of nonpolar MAI and PbI2 are most stable. The corresponding band structures reveal surface states originating from the conduction band. Despite the presence of such additional surface states, our stable reconstructed surface models do not introduce new states within the band gap. △ Less

Submitted 24 August, 2021; originally announced August 2021.

Comments: 12 pages in main manuscript, 3 pages Supplementary material, 16 figures

Assessing the $G_0W_0Γ^{(1)}_0$ approach: Beyond $G_0W_0$ with Hedin's full second-order self-energy contribution

Authors: Yanyong Wang, Patrick Rinke, Xinguo Ren

Abstract: We present and benchmark a self-energy approach for quasiparticle energy calculations that goes beyond Hedin's $GW$ approximation by adding the full second-order self-energy (FSOS-$W$) contribution. The FSOS-$W$ diagram involves two screened Coulomb interaction ($W$) lines and adding the FSOS-$W$ to the $GW$ self-energy can be interpreted as first-order vertex correction to $GW$ ($GWΓ^{(1)}$). Our… ▽ More We present and benchmark a self-energy approach for quasiparticle energy calculations that goes beyond Hedin's $GW$ approximation by adding the full second-order self-energy (FSOS-$W$) contribution. The FSOS-$W$ diagram involves two screened Coulomb interaction ($W$) lines and adding the FSOS-$W$ to the $GW$ self-energy can be interpreted as first-order vertex correction to $GW$ ($GWΓ^{(1)}$). Our FSOS-$W$ implementation is based on the resolution-of-identity technique and exhibits better than $O(N^5)$ scaling with system size for small to medium-sized molecules. We then present one-shot $GWΓ^{(1)}$ ($G_0W_0Γ^{(1)}_0$) benchmarks for the $GW$100 test set and a set of 24 acceptor molecules. For semilocal or hybrid density functional theory starting points, $G_0W_0Γ^{(1)}_0$ systematically outperforms $G_0W_0$ for the first vertical ionization potentials (vIPs) and electron affinities (vEAs) of both test sets. Finally, we demonstrate that a static FSOS-$W$ self-energy significantly underestimates the quasiparticle energies. △ Less

Submitted 16 May, 2021; originally announced May 2021.

Comments: 14 pages, 6 figures, 4 tables

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Authors: Jari Järvi, Benjamin Alldritt, Ondřej Krejčí, Milica Todorović, Peter Liljeroth, Patrick Rinke

Abstract: Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In thi… ▽ More Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In this fresh approach, cross-disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption. △ Less

Submitted 12 April, 2021; originally announced April 2021.

Comments: 9 pages, 4 figures, 1 table

Journal ref: Adv. Funct. Mater. 31 (2021) 2010853

Electronic Characterization of a Charge-Transfer Complex Monolayer on Graphene

Authors: Avijit Kumar, Kaustuv Banerjee, Mikko M. Ervasti, Shawulienu Kezilebieke, Marc Dvorak, Patrick Rinke, Ari Harju, Peter Liljeroth

Abstract: Organic charge-transfer complexes (CTCs) formed by strong electron acceptor and strong electron donor molecules are known to exhibit exotic effects such as superconductivity and charge density waves. We present a low-temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) study of a two-dimensional (2D) monolayer CTC of tetrathiafulvalene (TTF) and fluorinated tetracyanoquinodimeth… ▽ More Organic charge-transfer complexes (CTCs) formed by strong electron acceptor and strong electron donor molecules are known to exhibit exotic effects such as superconductivity and charge density waves. We present a low-temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) study of a two-dimensional (2D) monolayer CTC of tetrathiafulvalene (TTF) and fluorinated tetracyanoquinodimethane (F4TCNQ), self-assembled on the surface of oxygen-intercalated epitaxial graphene on Ir(111) (G/O/Ir(111)). We confirm the formation of the charge-transfer complex by dI/dV spectroscopy and direct imaging of the singly-occupied molecular orbitals. High-resolution spectroscopy reveals a gap at zero bias, suggesting the formation of a correlated ground state at low temperatures. These results point to the possibility to realize and study correlated ground states in charge-transfer complex monolayers on weakly interacting surfaces. △ Less

Submitted 27 May, 2021; v1 submitted 18 February, 2021; originally announced February 2021.

Comments: Manuscript and Supplementary Information, 36 pages

Journal ref: ACS Nano 15(6), 9945 (2021)

Atomic and electronic structure of cesium lead triiodide surfaces

Authors: Azimatu Seidu, Marc Dvorak, Patrick Rinke, Jingrui Li

Abstract: The (001) surface of the emerging photovoltaic material cesium lead triiodide (CsPbI3 ) is studied. Using first-principles methods, we investigate the atomic and electronic structure of cubic (α) and orthorhombic (γ) CsPbI3 . For both phases, we find that CsI-termination is more stable than PbI2-termination. For the CsI-terminated surface, we then compute and analyse the surface phase diagram. We… ▽ More The (001) surface of the emerging photovoltaic material cesium lead triiodide (CsPbI3 ) is studied. Using first-principles methods, we investigate the atomic and electronic structure of cubic (α) and orthorhombic (γ) CsPbI3 . For both phases, we find that CsI-termination is more stable than PbI2-termination. For the CsI-terminated surface, we then compute and analyse the surface phase diagram. We observe that surfaces with added or removed units of nonpolar CsI and PbI2 are most stable. The corresponding band structures reveal that the α phase exhibits surface states that derive from the conduction band. The surface reconstructions do not introduce new states in the band gap of CsPbI3, but for the α phase we find additional surface states at the conduction band edge. △ Less

Submitted 9 November, 2020; originally announced November 2020.

Electron-phonon coupling in d-electron solids: A temperature dependent study of rutile TiO2 by first-principles theory and two-photon photoemission

Authors: Honghui Shang, Adam Argondizzo, Shijing Tan, Jin Zhao, Patrick Rinke, Christian Carbogno, Matthias Scheffler, Hrvoje Petek

Abstract: Rutile TiO2 is a paradigmatic transition metal oxide with applications in optics, electronics, photocatalysis, etc., that are subject to pervasive electron-phonon interaction. To understand how energies of its electronic bands, and in general semiconductors or metals where the frontier orbitals have a strong d-band character, depend on temperature, we perform a comprehensive theoretical and experi… ▽ More Rutile TiO2 is a paradigmatic transition metal oxide with applications in optics, electronics, photocatalysis, etc., that are subject to pervasive electron-phonon interaction. To understand how energies of its electronic bands, and in general semiconductors or metals where the frontier orbitals have a strong d-band character, depend on temperature, we perform a comprehensive theoretical and experimental study of the effects of electron-phonon (e-p) interactions. In a two-photon photoemission (2PP) spectroscopy study we observe an unusual temperature dependence of electronic band energies within the conduction band of reduced rutile TiO2, which is contrary to the well understood sp-band semiconductors and points to a so far unexplained dichotomy in how the e-p interactions affect differently the materials where the frontier orbitals are derived from the sp- and d-orbitals. To develop a broadly applicable model, we employ state-of-the-art first-principles calculations that explain how phonons promote interactions between the Ti-3d orbitals of the conduction band within the octahedral crystal field. The characteristics differences in e-p interactions experienced by the Ti 3d-orbitals of rutile TiO2 crystal lattice are contrasted with the more familiar behavior of the Si 2s-orbitals of stishovite SiO2 polymorph, in which the frontier 2s-orbital experience a similar crystal field with the opposite effect... △ Less

Submitted 8 September, 2020; originally announced September 2020.

Journal ref: Physical Review RESEARCH(2019)

Real-Time Time-Dependent Density Functional Theory Implementation of Electronic Circular Dichroism Applied to Nanoscale Metal-Organic Clusters

Authors: Esko Makkonen, Tuomas P. Rossi, Ask Hjorth Larsen, Olga Lopez-Acevedo, Patrick Rinke, Mikael Kuisma, Xi Chen

Abstract: Electronic circular dichroism (ECD) is a powerful spectroscopical method for investigating chiral properties at the molecular level. ECD calculations with the commonly used linear-response time-dependent density functional theory (LR-TDDFT) framework can be prohibitively costly for large systems. To alleviate this problem, we present here an ECD implementation for the projector augmented-wave meth… ▽ More Electronic circular dichroism (ECD) is a powerful spectroscopical method for investigating chiral properties at the molecular level. ECD calculations with the commonly used linear-response time-dependent density functional theory (LR-TDDFT) framework can be prohibitively costly for large systems. To alleviate this problem, we present here an ECD implementation for the projector augmented-wave method in the real-time-propagation TDDFT (RT-TDDFT) framework in the open-source GPAW code. Our implementation supports both local atomic basis set and real-space finite-difference representations of wave functions. We benchmark our implementation against an existing LR-TDDFT implementation in GPAW for small chiral molecules. We then demonstrate the efficiency of our local atomic basis set implementation for a large hybrid nanocluster. △ Less

Submitted 16 July, 2020; originally announced July 2020.

Efficient Cysteine Conformer Search with Bayesian Optimization

Authors: Lincan Fang, Esko Makkonen, Milica Todorovic, Patrick Rinke, Xi Chen

Abstract: Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our pro… ▽ More Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only one thousand single-point calculations and approximately thirty structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. △ Less

Submitted 26 June, 2020; originally announced June 2020.

A balanced treatment of static and dynamic correlation in free- and Mg-porphyrin

Authors: Marc Dvorak, Patrick Rinke

Abstract: We present an ab-initio dynamical configuration interaction (DCI) study of free- and Mg-porphyrin. DCI is a recently developed active space theory based on the Löwdin downfolding technique. In the active space, static correlation is described exactly with full configuration interaction. In the high energy, dynamically correlated subspace, we treat correlation at the quasiparticle level in the… ▽ More We present an ab-initio dynamical configuration interaction (DCI) study of free- and Mg-porphyrin. DCI is a recently developed active space theory based on the Löwdin downfolding technique. In the active space, static correlation is described exactly with full configuration interaction. In the high energy, dynamically correlated subspace, we treat correlation at the quasiparticle level in the $GW$ approximation of Green's function theory. The final theory combines wave function and Green's function methods to give a balanced description of static and dynamic correlation. The theory and algorithm give a multireference treatment of ground and excited states for low computational cost. The four orbital Gouterman model of porphyrin offers an ideal active space in a large, correlated system to test the cost and accuracy of the embedding for large systems. Our parameter free, fully ab-initio DCI calculations in the minimal four-level active space agree well with both experiment and more expensive benchmark theories for the $Q_x$ and $Q_y$ transitions of free- and Mg-porphyrin. Studying the convergence of the excitation energies suggests that DCI correctly captures size extensive correlation effects, making it a promising active space theory for large, strongly-correlated systems. △ Less

Submitted 17 June, 2020; originally announced June 2020.

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

Authors: Jari Järvi, Patrick Rinke, Milica Todorović

Abstract: Identifying the atomic structure of organic-inorganic interfaces is challenging with our current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study… ▽ More Identifying the atomic structure of organic-inorganic interfaces is challenging with our current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among 8 unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, like BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials. △ Less

Submitted 1 December, 2020; v1 submitted 13 February, 2020; originally announced February 2020.

Comments: 16 pages, 10 figures, 5 tables. Peer-reviewed version. Published in the Beilstein Journal of Nanotechnology

Journal ref: Beilstein J. Nanotechnol. 11 (2020) 1577-1589

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Authors: Annika Stuke, Christian Kunkel, Dorothea Golze, Milica Todorović, Johannes T. Margraf, Karsten Reuter, Patrick Rinke, Harald Oberhofer

Abstract: Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD),… ▽ More Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. We here supply a diverse benchmark spectroscopy dataset of 61,489 molecules extracted from organic crystals in the Cambridge Structural Database (CSD), denoted OE62. Molecular equilibrium geometries are reported at the Perdew-Burke-Ernzerhof (PBE) level of density functional theory (DFT) including van der Waals corrections for all 62k molecules. For these geometries, OE62 supplies total energies and orbital eigenvalues at the PBE and the PBE hybrid (PBE0) functional level of DFT for all 62k molecules in vacuum as well as at the PBE0 level for a subset of 30,876 molecules in (implicit) water. For 5,239 molecules in vacuum, the dataset provides quasiparticle energies computed with many-body perturbation theory in the $G_0W_0$ approximation with a PBE0 starting point (denoted GW5000 in analogy to the GW100 benchmark set (M. van Setten et al. J. Chem. Theory Comput. 12, 5076 (2016))). △ Less

Submitted 24 January, 2020; originally announced January 2020.

The GW compendium: A practical guide to theoretical photoemission spectroscopy

Authors: Dorothea Golze, Marc Dvorak, Patrick Rinke

Abstract: The GW approximation in electronic structure theory has become a widespread tool for predicting electronic excitations in chemical compounds and materials. In the realm of theoretical spectroscopy, the GW method provides access to charged excitations as measured in direct or inverse photoemission spectroscopy. The number of GW calculations in the past two decades has exploded with increased comput… ▽ More The GW approximation in electronic structure theory has become a widespread tool for predicting electronic excitations in chemical compounds and materials. In the realm of theoretical spectroscopy, the GW method provides access to charged excitations as measured in direct or inverse photoemission spectroscopy. The number of GW calculations in the past two decades has exploded with increased computing power and modern codes. The success of GW can be attributed to many factors: favorable scaling with respect to system size, a formal interpretation for charged excitation energies, the importance of dynamical screening in real systems, and its practical combination with other theories. In this review, we provide an overview of these formal and practical considerations. We expand, in detail, on the choices presented to the scientist performing GW calculations for the first time. We also give an introduction to the many-body theory behind GW, a review of modern applications like molecules and surfaces, and a perspective on methods which go beyond conventional GW calculations. This review addresses chemists, physicists and material scientists with an interest in theoretical spectroscopy. It is intended for newcomers to GW calculations but can also serve as an alternative perspective for experts and an up-to-date source of computational techniques. △ Less

Submitted 10 December, 2019; originally announced December 2019.

Comments: Review article. https://www.frontiersin.org/articles/10.3389/fchem.2019.00377/full

Journal ref: Front. Chem. 7:377 (2019)

Boron doping in gallium oxide from first principles

Authors: Jouko Lehtomäki, Jingrui Li, Patrick Rinke

Abstract: We study the feasibility of boron doping in gallium oxide ($\text{Ga}_2\text{O}_3$) for neutron detection. $\text{Ga}_2\text{O}_3$ is a wide band-gap, radiation hard material which has potential for neutron detection if it can be doped with a neutron active element. We investigate the boron-10 isotope as a possible neutron active dopant. Intrinsic and boron induced defects in… ▽ More We study the feasibility of boron doping in gallium oxide ($\text{Ga}_2\text{O}_3$) for neutron detection. $\text{Ga}_2\text{O}_3$ is a wide band-gap, radiation hard material which has potential for neutron detection if it can be doped with a neutron active element. We investigate the boron-10 isotope as a possible neutron active dopant. Intrinsic and boron induced defects in $\text{Ga}_2\text{O}_3$ are studied with semi-local and hybrid density-functional-theory calculations. We find that in growth conditions favourable for boron, boron substitutional defects are likely to form making boron doping of $\text{Ga}_2\text{O}_3$ feasible. △ Less

Submitted 4 July, 2020; v1 submitted 16 October, 2019; originally announced October 2019.

Comments: 9 pages, 7 figures, 5 tables

Data-driven materials science: status, challenges and perspectives

Authors: Lauri Himanen, Amber Geurts, Adam S. Foster, Patrick Rinke

Abstract: Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials data sets that are too big or complex for traditional human reasoning - typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress… ▽ More Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials data sets that are too big or complex for traditional human reasoning - typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, we discuss the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data infrastructures. We also review key successes and challenges so far, providing a perspective on the future development of the field. △ Less

Submitted 19 August, 2019; v1 submitted 12 July, 2019; originally announced July 2019.

Comments: Added new entries to the materials data infrastucture tables, updated references and affiliations

Formation of Graphene atop a Si adlayer on the C-face of SiC

Authors: Jun Li, Qingxiao Wang, Guowei He, Michael Widom, Lydia Nemec, Volker Blum, Moon Kim, Patrick Rinke, Randall M. Feenstra

Abstract: The structure of the SiC(000-1) surface, the C-face of the {0001} SiC surfaces, is studied as a function of temperature and of pressure in a gaseous environment of disilane (Si2H6). Various surface reconstructions are observed, both with and without the presence of an overlying graphene layer (which spontaneously forms at sufficiently high temperatures). Based on cross-sectional scanning transmiss… ▽ More The structure of the SiC(000-1) surface, the C-face of the {0001} SiC surfaces, is studied as a function of temperature and of pressure in a gaseous environment of disilane (Si2H6). Various surface reconstructions are observed, both with and without the presence of an overlying graphene layer (which spontaneously forms at sufficiently high temperatures). Based on cross-sectional scanning transmission electron microscopy measurements, the interface structure that forms in the presence of the graphene is found to contain 1.4 - 1.7 monolayers (ML) of Si, a somewhat counter-intuitive result since, when the graphene forms, the system is actually under C-rich conditions. Using ab initio thermodynamics, it is demonstrated that there exists a class of Si-rich surfaces containing about 1.3 ML of Si that are stable on the surface (even under C-rich conditions) at temperatures above about 400 K. The structures that thus form consist of Si adatoms atop a Si adlayer on the C-face of SiC, with or without the presence of overlying graphene. △ Less

Submitted 13 August, 2019; v1 submitted 10 May, 2019; originally announced May 2019.

Comments: 19 pages with 8 figures, along with 6 pages of Supplemental Material; v2 adds refs. 56,59-62 all being prior work, along with several paragraphs in the Discussion or Summary sections of the manuscript discussing this prior work; v3 corrects several typographical errors

Journal ref: Phys. Rev. Materials 3, 084006 (2019)

DScribe: Library of Descriptors for Machine Learning in Materials Science

Authors: Lauri Himanen, Marc O. J. Jäger, Eiaki V. Morooka, Filippo Federici Canova, Yashasvi S. Ranawat, David Z. Gao, Patrick Rinke, Adam S. Foster

Abstract: DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine… ▽ More DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0. △ Less

Submitted 18 April, 2019; originally announced April 2019.

Journal ref: Comp. Phys. Comm. 247 (2020) 106949

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

Authors: Annika Stuke, Milica Todorović, Matthias Rupp, Christian Kunkel, Kunal Ghosh, Lauri Himanen, Patrick Rinke

Abstract: Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules s… ▽ More Instant machine learning predictions of molecular properties are desirable for materials design, but the predictive power of the methodology is mainly tested on well-known benchmark datasets. Here, we investigate the performance of machine learning with kernel ridge regression (KRR) for the prediction of molecular orbital energies on three large datasets: the standard QM9 small organic molecules set, amino acid and dipeptide conformers, and organic crystal-forming molecules extracted from the Cambridge Structural Database. We focus on prediction of highest occupied molecular orbital (HOMO) energies, computed at density-functional level of theory. Two different representations that encode molecular structure are compared: the Coulomb matrix (CM) and the many-body tensor representation (MBTR). We find that KRR performance depends significantly on the chemistry of the underlying dataset and that the MBTR is superior to the CM, predicting HOMO energies with a mean absolute error as low as 0.09 eV. To demonstrate the power of our machine learning method, we apply our model to structures of 10k previously unseen molecules. We gain instant energy predictions that allow us to identify interesting molecules for future applications. △ Less

Submitted 25 March, 2019; v1 submitted 20 December, 2018; originally announced December 2018.

Comments: 16 pages, 13 figures

Dynamical configuration interaction: Quantum embedding that combines wave functions and Green's functions

Authors: Marc Dvorak, Patrick Rinke

Abstract: We present the concept, derivation, and implementation of dynamical configuration interaction, a quantum embedding theory that combines Green's function methodology with the many-body wave function. In a strongly-correlated active space, we use full configuration interaction (CI) to describe static correlation exactly. We add energy dependent corrections to the CI Hamiltonian which, in principle,… ▽ More We present the concept, derivation, and implementation of dynamical configuration interaction, a quantum embedding theory that combines Green's function methodology with the many-body wave function. In a strongly-correlated active space, we use full configuration interaction (CI) to describe static correlation exactly. We add energy dependent corrections to the CI Hamiltonian which, in principle, include all remaining correlation derived from the bath space surrounding the active space. Next, we replace the exact Hamiltonian in the bath with one of excitations defined over a correlated ground state. This transformation is naturally suited to the methodology of many-body Green's functions. In this space, we use a modified $GW$/Bethe-Salpeter equation procedure to calculate excitation energies. Combined with an estimate of the ground state energy in the bath, we can efficiently compute the energy dependent corrections, which correlate the full set of orbitals, for very low computational cost. We present dimer dissociation curves for H$_2$ and N$_2$ in good agreement with exact results. Additionally, excited states of N$_2$ and C$_2$ are in excellent agreement with benchmark theory and experiment. By combining the strengths of two disciplines, we achieve a balanced description of static and dynamic correlation in a fully ab-initio, systematically improvable framework. △ Less

Submitted 29 October, 2018; originally announced October 2018.

Journal ref: Phys. Rev. B 99, 115134 (2019)

A quantum embedding theory in the screened Coulomb interaction: Combining configuration interaction with GW/BSE

Authors: Marc Dvorak, Dorothea Golze, Patrick Rinke

Abstract: We present a new quantum embedding theory called dynamical configuration interaction (DCI) that combines wave function and Green's function theories. DCI captures static correlation in a correlated subspace with configuration interaction and couples to high-energy, dynamic correlation outside the subspace with many-body perturbation theory based on Green's functions. In the correlated subspace, we… ▽ More We present a new quantum embedding theory called dynamical configuration interaction (DCI) that combines wave function and Green's function theories. DCI captures static correlation in a correlated subspace with configuration interaction and couples to high-energy, dynamic correlation outside the subspace with many-body perturbation theory based on Green's functions. In the correlated subspace, we use a wave function description to avoid embedding the two-particle vertex, which greatly simplifies the frequency structure of the embedding. DCI takes the strengths of both theories to balance static and dynamic correlation in a single, fully ab-initio embedding concept. We show that treating high-energy correlation up to the $GW$ and Bethe-Salpeter equation level is sufficient even for challenging multi-reference problems. Our theory treats ground and excited states on equal footing, and we compute the dissociation curve of N$_2$, vertical excitation energies of N$_2$ and C$_2$, and the ionization spectrum of benzene in excellent agreement with high level quantum chemistry methods and experiment. △ Less

Submitted 20 November, 2019; v1 submitted 29 October, 2018; originally announced October 2018.

Journal ref: Phys. Rev. Materials 3, 070801 (2019)

Multi-scale model for the structure of hybrid perovskites: Analysis of charge migration in disordered MAPbI$_3$ structures

Authors: Jari Järvi, Jingrui Li, Patrick Rinke

Abstract: We have developed a multi-scale model for organic-inorganic hybrid perovskites (HPs) that applies quantum mechanical (QM) calculations of small HP supercell models to large coarse-grained structures. With a mixed quantum-classical hopping model, we have studied the effects of cation disorder on charge mobilities in HPs, which is a key feature to optimize their photovoltaic performance. Our multi-s… ▽ More We have developed a multi-scale model for organic-inorganic hybrid perovskites (HPs) that applies quantum mechanical (QM) calculations of small HP supercell models to large coarse-grained structures. With a mixed quantum-classical hopping model, we have studied the effects of cation disorder on charge mobilities in HPs, which is a key feature to optimize their photovoltaic performance. Our multi-scale model parametrizes the interaction between neighboring methylammonium cations (MA$^+$) in the prototypical HP material, methylammonium lead triiodide (CH$_3$NH$_3$PbI$_3$, or MAPbI$_3$). For the charge mobility analysis with our hopping model, we solved the QM site-to-site hopping probabilities analytically and computed the nearest-neighbor electronic coupling energies from the band structure of MAPbI$_3$ with density-functional theory. We investigated the charge mobility in various MAPbI$_3$ supercell models of ordered and disordered MA$^+$ cations. Our results indicate a structure-dependent mobility, in the range of 50$-$66 cm$^2$V$^{-1}$s$^{-1}$, with the highest observed in the ordered tetragonal phase. △ Less

Submitted 11 October, 2018; v1 submitted 14 June, 2018; originally announced June 2018.

Comments: 28 pages, 13 figures

Journal ref: Jari Järvi et al 2018 New J. Phys. 20 103013

All-Electron, Real-Space Perturbation Theory for Homogeneous Electric Fields: Theory, Implementation, and Application within DFT

Authors: Honghui Shang, Nathaniel Raimbault, Patrick Rinke, Matthias Scheffler, Mariana Rossi, Christian Carbogno

Abstract: Within density-functional theory, perturbation theory~(PT) is the state-of-the-art formalism for assessing the response to homogeneous electric fields and the associated material properties, e.g., polarizabilities, dielectric constants, and Raman intensities. Here we derive a real-space formulation of PT and present an implementation within the all-electron, numeric atom-centered orbitals electron… ▽ More Within density-functional theory, perturbation theory~(PT) is the state-of-the-art formalism for assessing the response to homogeneous electric fields and the associated material properties, e.g., polarizabilities, dielectric constants, and Raman intensities. Here we derive a real-space formulation of PT and present an implementation within the all-electron, numeric atom-centered orbitals electronic structure code FHI-aims that allows for massively-parallel calculations. As demonstrated by extensive validation, this allows the rapid computation of accurate response properties of molecules and solids. As an application showcase, we present harmonic and anharmonic Raman spectra, the latter obtained by combining hundreds of thousands of PT calculations with \textit{ab initio} molecular dynamics. By using the PBE exchange-correlation functional with many-body van der Waals corrections, we obtain spectra in good agreement with experiment especially with respect to lineshapes for the isolated paracetamol molecule and two polymorphs of the paracetamol crystal. △ Less

Submitted 21 June, 2018; v1 submitted 2 March, 2018; originally announced March 2018.

Journal ref: New Journal of Physics (2018)

Theory of excitation transfer between two-dimensional semiconductor and molecular layers

Authors: Judith Specht, Eike Verdenhalven, Björn Bieniek, Patrick Rinke, Andreas Knorr, Marten Richter

Abstract: The geometry-dependent energy transfer rate from an electrically pumped inorganic semiconductor quantum well into an organic molecular layer is studied theoretically. We focus on Förster-type nonradiative excitation transfer between the organic and inorganic layer and include quasi-momentum conservation and intermolecular coupling between the molecules in the organic film. (Transition) partial cha… ▽ More The geometry-dependent energy transfer rate from an electrically pumped inorganic semiconductor quantum well into an organic molecular layer is studied theoretically. We focus on Förster-type nonradiative excitation transfer between the organic and inorganic layer and include quasi-momentum conservation and intermolecular coupling between the molecules in the organic film. (Transition) partial charges calculated from density-functional theory are used to calculate the coupling elements. The partial charges describe the spatial charge distribution and go beyond the common dipole-dipole interaction. We find that the transfer rates are highly sensitive to variations in the geometry of the hybrid inorganic/organic system. For instance, the transfer efficiency is improved by orders of magnitude by tuning the relative orientation and positioning of the molecules. Also, the operating regime is identified where in-scattering dominates over unwanted back-scattering from the molecular layer into the substrate. △ Less

Submitted 24 November, 2017; originally announced November 2017.

Journal ref: Phys. Rev. Applied 9, 044025 (2018)

First-Principles Supercell Calculations of Small Polarons with Proper Account for Long-Range Polarization Effects

Authors: Sebastian Kokott, Sergey V Levchenko, Patrick Rinke, Matthias Scheffler

Abstract: We present a density functional theory (DFT) based supercell approach for modeling small polarons with proper account for the long-range elastic response of the material. Our analysis of the supercell dependence of the polaron properties (e.g., atomic structure, binding energy, and the polaron level) reveals long-range electrostatic effects and the electron-phonon interaction as the two main contr… ▽ More We present a density functional theory (DFT) based supercell approach for modeling small polarons with proper account for the long-range elastic response of the material. Our analysis of the supercell dependence of the polaron properties (e.g., atomic structure, binding energy, and the polaron level) reveals long-range electrostatic effects and the electron-phonon interaction as the two main contributors. We develop a correction scheme for DFT polaron calculations that significantly reduces the dependence of polaron properties on the DFT exchange-correlation functional and the size of the supercell in the limit of strong electron-phonon coupling. Using our correction approach, we present accurate all-electron full-potential DFT results for small polarons in rocksalt MgO and rutile TiO$_2$. △ Less

Submitted 10 October, 2017; originally announced October 2017.

Efficient Bayesian Inference of Atomistic Structure in Complex Functional Materials

Authors: Milica Todorović, Michael U. Gutmann, Jukka Corander, Patrick Rinke

Abstract: Tailoring the functional properties of advanced organic/inorganic heterogeonous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computat… ▽ More Tailoring the functional properties of advanced organic/inorganic heterogeonous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device. Atomistic quantum mechanical simulation methods deliver accurate energies and properties for individual configurations, however, finding the most favourable configurations remains computationally prohibitive. We propose a 'building block'-based Bayesian Optimisation Structure Search (BOSS) approach for addressing extended organic/inorganic interface problems and demonstrate its feasibility in a molecular surface adsorption study. In BOSS, a likelihood-free Bayesian scheme accelerates the identification of material energy landscapes with the number of sampled configurations during active learning, enabling structural inference with high chemical accuracy and featuring large simulation cells. This allowed us to identify several most favourable molecular adsorption configurations for $\mathrm{C}_{60}$ on the (101) surface of $\mathrm{TiO}_2$ anatase and clarify the key molecule-surface interactions governing structural assembly. Inferred structures were in good agreement with detailed experimental images of this surface adsorbate, demonstrating good predictive power of BOSS and opening the route towards large-scale surface adsorption studies of molecular aggregates and films. △ Less

Submitted 12 March, 2019; v1 submitted 30 August, 2017; originally announced August 2017.

Comments: 8 pages, 5 figures. Peer-reviewed version

Multi-scale model for disordered hybrid perovskites: the concept of organic cation pair modes

Authors: Jingrui Li, Jari Järvi, Patrick Rinke

Abstract: We have studied the properties of the prototype hybrid organic-inorganic perovskite $\text{CH}_3^{}\text{NH}_3^{}\text{PbI}_3^{}$ using relativistic density functional theory (DFT). For our analysis we introduce the concept of $\text{CH}_3^{}\text{NH}_3^+$ "pair modes", that is, characteristic relative orientations of two neighboring $\text{CH}_3^{}\text{NH}_3^+$ cations. In our previous work [Phy… ▽ More We have studied the properties of the prototype hybrid organic-inorganic perovskite $\text{CH}_3^{}\text{NH}_3^{}\text{PbI}_3^{}$ using relativistic density functional theory (DFT). For our analysis we introduce the concept of $\text{CH}_3^{}\text{NH}_3^+$ "pair modes", that is, characteristic relative orientations of two neighboring $\text{CH}_3^{}\text{NH}_3^+$ cations. In our previous work [Phys. Rev. B \textbf{94}, 045201 (2016)] we identified two preferential orientations that a single $\text{CH}_3^{}\text{NH}_3^+$ cation adopts in a unit cell. The total number of relevant pairs can be reduced from the resulting 196 combinations to only 25 by applying symmetry operations. DFT results of several $2\!\times\!2\!\times\!2$ supercell models reveal the dependence of the total energy, band gap and band structure on the distribution of $\text{CH}_3^{}\text{NH}_3^+$ cations and the pair modes. We have then analyzed the pair-mode distribution of a series of $4\!\times\!4\!\times\!4$ supercell models with disordered $\text{CH}_3^{}\text{NH}_3^+$ cations. Our results show that diagonally-oriented $\text{CH}_3^{}\text{NH}_3^+$ cations are rare in optimized $\text{CH}_3^{}\text{NH}_3^{}\text{PbI}_3^{}$ supercell structures. In the prevailing pair modes, the $\text{C--N}$ bonds of the two neighboring $\text{CH}_3^{}\text{NH}_3^+$ cations are aligned approximately vertically. Furthermore, we fit the coefficients of a pair-mode expansion to our supercell DFT reference structures. The pair-mode model can then be used to quickly estimate the energies of disordered perovskite structures. Our pair-mode concept provides combined atomistic-statistical insight into disordered structures in bulk hybrid perovskite materials. △ Less

Submitted 15 May, 2018; v1 submitted 30 March, 2017; originally announced March 2017.

Comments: 23 pages (including a manuscript of 13 pages and a supplemental material of 10 pages); 11 figures and 5 tables in the manuscript; 4 figures in the supplemental material

Journal ref: Phys. Rev. B 98, 045201 (2018)

Lattice Dynamics Calculations based on Density-functional Perturbation Theory in Real Space

Authors: Honghui Shang, Christian Carbogno, Patrick Rinke, Matthias Scheffler

Abstract: A real-space formalism for density-functional perturbation theory (DFPT) is derived and applied for the computation of harmonic vibrational properties in molecules and solids. The practical implementation using numeric atom-centered orbitals as basis functions is demonstrated exemplarily for the all-electron Fritz Haber Institute ab initio molecular simulations (FHI-aims) package. The convergence… ▽ More A real-space formalism for density-functional perturbation theory (DFPT) is derived and applied for the computation of harmonic vibrational properties in molecules and solids. The practical implementation using numeric atom-centered orbitals as basis functions is demonstrated exemplarily for the all-electron Fritz Haber Institute ab initio molecular simulations (FHI-aims) package. The convergence of the calculations with respect to numerical parameters is carefully investigated and a systematic comparison with finite-difference approaches is performed both for finite (molecules) and extended (periodic) systems. Finally, the scaling tests and scalability tests on massively parallel computer systems demonstrate the computational efficiency. △ Less

Submitted 7 March, 2017; v1 submitted 12 October, 2016; originally announced October 2016.

Journal ref: Computer Physics Communications (2017)

Towards efficient orbital-dependent density functionals for weak and strong correlation

Authors: Igor Ying Zhang, Patrick Rinke, John P. Perdew, Matthias Scheffler

Abstract: We present a new paradigm for the design of exchange-correlation functionals in density-functional theory. Electron pairs are correlated explicitly by means of the recently developed second order Bethe-Goldstone equation (BGE2) approach. Here we propose a screened BGE2 (sBGE2) variant that efficiently regulates the coupling of a given electron pair. sBGE2 correctly dissociates H$_2$ and H$_2^+$, a… ▽ More We present a new paradigm for the design of exchange-correlation functionals in density-functional theory. Electron pairs are correlated explicitly by means of the recently developed second order Bethe-Goldstone equation (BGE2) approach. Here we propose a screened BGE2 (sBGE2) variant that efficiently regulates the coupling of a given electron pair. sBGE2 correctly dissociates H$_2$ and H$_2^+$, a problem that has been regarded as a great challenge in density-functional theory for a long time. The sBGE2 functional is then taken as a building block for an orbital-dependent functional, termed ZRPS, which is a natural extension of the PBE0 hybrid functional. While worsening the good performance of sBGE2 in H$_2$ and H$_2^{+}$, ZRPS yields a remarkable and consistent improvement over other density functionals across various chemical environments from weak to strong correlation. △ Less

Submitted 27 August, 2016; originally announced August 2016.

Comments: 5 pages, 2 figures

Journal ref: Phys. Rev. Lett. 117, 133002 (2016)

Wave-function inspired density functional applied to the H$_2$/H$_2^+$ challenge

Authors: Igor Ying Zhang, Patrick Rinke, Matthias Scheffler

Abstract: We start from the Bethe-Goldstone equation (BGE) to derive a simple orbital-dependent correlation functional -- BGE2 -- which terminates the BGE expansion at the second-order, but retains the self-consistent coupling of electron-pair orrelations. We demonstrate that BGE2 is size consistent and one-electron "self-correlation" free. The electron-pair correlation coupling ensures the correct H$_2$ di… ▽ More We start from the Bethe-Goldstone equation (BGE) to derive a simple orbital-dependent correlation functional -- BGE2 -- which terminates the BGE expansion at the second-order, but retains the self-consistent coupling of electron-pair orrelations. We demonstrate that BGE2 is size consistent and one-electron "self-correlation" free. The electron-pair correlation coupling ensures the correct H$_2$ dissociation limit and gives a finite correlation energy for any system even if it has a no energy gap. BGE2 provides a good description of both H$_2$ and H$_2^+$ dissociation, which is regarded as a great challenge in density functional theory (DFT). We illustrate the behavior of BGE2 analytically by considering H$_2$ in a minimal basis. Our analysis shows that BGE2 captures essential features of the adiabatic connection path that current state-of-the-art DFT approximations do not. △ Less

Submitted 13 April, 2016; originally announced April 2016.

Comments: 14 pages, 7 figures

Journal ref: New J. Phys. 18, 073026 (2016)

Atomic structure of metal-halide perovskites from first principles: The chicken-and-egg paradox of the organic-inorganic interaction

Authors: Jingrui Li, Patrick Rinke

Abstract: We have studied the prototype hybrid organic-inorganic perovskite CH3NH3PbI3 and its three close relatives, CH3NH3SnI3, CH3NH3PbCl3 and CsPbI3, using relativistic density function theory. The long-range van der Waals (vdW) interactions were incorporated into the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional using the Tkatchenko-Scheffler pairwise scheme. Our results reveal that hydr… ▽ More We have studied the prototype hybrid organic-inorganic perovskite CH3NH3PbI3 and its three close relatives, CH3NH3SnI3, CH3NH3PbCl3 and CsPbI3, using relativistic density function theory. The long-range van der Waals (vdW) interactions were incorporated into the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional using the Tkatchenko-Scheffler pairwise scheme. Our results reveal that hydrogen bonding, which is well described by the PBE functional, plays a decisive role for the structural parameters of these systems, including the position and orientation of the organic cation as well as the deformation of the inorganic framework. The magnitude of the inorganic-framework deformation depends sensitively on the orientation of the organic cation, and directly influences the stability of the hybrid perovskites. Our results suggest that the organic and the inorganic components complement each other: The low symmetry of the organic cation is the origin of the inorganic-framework deformation, which then aids the overall stabilization of the hybrid perovskite structure. This stabilization is indirectly affected by vdW interactions, which lead to smaller unit-cell volumes than in PBE and therefore modulate the interaction between the organic cation and the inorganic framework. The vdW-induced lattice-constant corrections are system dependent and lead to PBE+vdW lattice constants in good agreement with experiment. Further insight is gained by analysing the vdW contributions. In all iodide-based hybrid perovskites the interaction between the organic cation and the iodide anions provides the largest lattice-constant change, followed by iodine-iodine and the organic cation - heavy-metal cation interaction. These corrections follow an almost linear dependence on the lattice constant within the range considered in our study, and are therefore approximately additive. △ Less

Submitted 11 March, 2016; v1 submitted 29 February, 2016; originally announced February 2016.

Comments: 12 pages (including 6 tables and 7 figures) plus 3 pages of supplemental material (3 tables)

Piecewise linearity in the $GW$ approximation for accurate quasiparticle energy predictions

Authors: Matthias Dauth, Fabio Caruso, Stephan Kümmel, Patrick Rinke

Abstract: We identify the deviation from the straight line error (DSLE) -- i.e., the spurious non-linearity of the total energy as a function of fractional particle number -- as the main source for the discrepancy between experimental vertical ionization energies and theoretical quasiparticle energies, as obtained from the $GW$ and $GW$+SOSEX approximations to many-body perturbation theory (MBPT). For self-… ▽ More We identify the deviation from the straight line error (DSLE) -- i.e., the spurious non-linearity of the total energy as a function of fractional particle number -- as the main source for the discrepancy between experimental vertical ionization energies and theoretical quasiparticle energies, as obtained from the $GW$ and $GW$+SOSEX approximations to many-body perturbation theory (MBPT). For self-consistent calculations, we show that $GW$ suffers from a small DSLE. Conversely, for perturbative $G_0W_0$ and $G_0W_0$+SOSEX calculations the DSLE depends on the starting point. We exploit this starting-point dependence to reduce (or completely eliminate) the DSLE. We find that the agreement with experiment increases as the DSLE reduces. DSLE-minimized schemes, thus, emerge as promising avenues for future developments in MBPT. △ Less

Submitted 7 December, 2015; originally announced December 2015.

Journal ref: Phys. Rev. B 93, 121115 (2016)

Self-Consistent Green Function Embedding for Advanced Electronic Structure Methods Based on a Dynamical Mean-Field Concept

Authors: Wael Chibani, Xinguo Ren, Matthias Scheffler, Patrick Rinke

Abstract: We present an embedding scheme for periodic systems that facilitates the treatment of the physically important part (here the unit cell) with advanced electronic-structure methods, that are computationally too expensive for periodic systems. The rest of the periodic system is treated with computationally less demanding approaches, e.g., Kohn-Sham density-functional theory, in a self- consistent ma… ▽ More We present an embedding scheme for periodic systems that facilitates the treatment of the physically important part (here the unit cell) with advanced electronic-structure methods, that are computationally too expensive for periodic systems. The rest of the periodic system is treated with computationally less demanding approaches, e.g., Kohn-Sham density-functional theory, in a self- consistent manner. Our scheme is based on the concept of dynamical mean-field theory (DMFT) formulated in terms of Green functions. In contrast to the original DMFT formulation for correlated model Hamiltonians, we here consider the unit cell as local embedded cluster in a first-principles way, that includes all electronic degrees of freedom. Our real-space dynamical mean-field embedding (RDMFE) scheme features two nested Dyson equations, one for the embedded cluster and another for the periodic surrounding. The total energy is computed from the resulting Green functions. The performance of our scheme is demonstrated by treating the embedded region with hybrid functionals and many-body perturbation theory in the GW approach for simple bulk systems. The total energy and the density of states converge rapidly with respect to the computational parameters and approach their bulk limit with increasing cluster (i.e., unit cell) size. △ Less

Submitted 11 June, 2015; originally announced June 2015.

Journal ref: Physical Review B 93 , 165106 (2016)

Why graphene growth is very different on the C face than on the Si face of SiC: Insights from surface equilibria and the (3$\times$3)-3C-SiC($\bar{\text{1}}\bar{\text{1}}\bar{\text{1}}$) reconstruction

Authors: Lydia Nemec, Florian Lazarevic, Patrick Rinke, Matthias Scheffler, Volker Blum

Abstract: We address the stability of the surface phases that occur on the C-side of 3C-SiC($\bar{1} \bar{1} \bar{1}$) at the onset of graphene formation. In this growth range, experimental reports reveal a coexistence of several surface phases. This coexistence can be explained by a Si-rich model for the unknown (3$\times$3) reconstruction, the known (2$\times$2)$_{C}$ adatom phase, and the graphene covere… ▽ More We address the stability of the surface phases that occur on the C-side of 3C-SiC($\bar{1} \bar{1} \bar{1}$) at the onset of graphene formation. In this growth range, experimental reports reveal a coexistence of several surface phases. This coexistence can be explained by a Si-rich model for the unknown (3$\times$3) reconstruction, the known (2$\times$2)$_{C}$ adatom phase, and the graphene covered (2$\times$2)$_{C}$ phase. By constructing an $ab$ $initio$ surface phase diagram using a van der Waals corrected density functional, we show that the formation of a well defined interface structure like the "buffer-layer" on the Si side is blocked by Si-rich surface reconstructions. △ Less

Submitted 30 March, 2015; originally announced March 2015.