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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…
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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.
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Submitted 20 April, 2026; v1 submitted 30 April, 2025;
originally announced May 2025.
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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI
Authors:
Petr Karpov,
Andreas Marek,
Tobias Melson,
Alexander Pöppl,
Victor Wen-zhe Yu,
Ben Hourahine,
Alberto Garcia,
William Dawson,
Yi Yao,
William Huhn,
Jonathan Moussa,
Sam Hall,
Reinhard Maurer,
Uthpala Herath,
Konstantin Lion,
Sebastian Kokott,
Volker Blum
Abstract:
In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims.
ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architect…
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In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims.
ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem.
ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others.
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Submitted 4 February, 2025;
originally announced February 2025.
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Roadmap on Data-Centric Materials Science
Authors:
Stefan Bauer,
Peter Benner,
Tristan Bereau,
Volker Blum,
Mario Boley,
Christian Carbogno,
C. Richard A. Catlow,
Gerhard Dehm,
Sebastian Eibl,
Ralph Ernstorfer,
Ádám Fekete,
Lucas Foppa,
Peter Fratzl,
Christoph Freysoldt,
Baptiste Gault,
Luca M. Ghiringhelli,
Sajal K. Giri,
Anton Gladyshev,
Pawan Goyal,
Jason Hattrick-Simpers,
Lara Kabalan,
Petr Karpov,
Mohammad S. Khorrami,
Christoph Koch,
Sebastian Kokott
, et al. (36 additional authors not shown)
Abstract:
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) a…
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Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.
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Submitted 1 May, 2024; v1 submitted 1 February, 2024;
originally announced February 2024.
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Physics-Informed Machine Learning for Modeling Turbulence in Supernovae
Authors:
Platon I. Karpov,
Chengkun Huang,
Iskandar Sitdikov,
Chris L. Fryer,
Stan Woosley,
Ghanshyam Pilania
Abstract:
Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capability for calculating turbulence closure.…
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Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSN), but current simulations must rely on subgrid models since direct numerical simulation (DNS) is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, Machine Learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network (CNN) to preserve the realizability condition of Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic (MHD) turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately-modeled turbulence on the explosion of these stars.
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Submitted 9 August, 2022; v1 submitted 17 May, 2022;
originally announced May 2022.
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Variational classical networks for dynamics in interacting quantum matter
Authors:
Roberto Verdel,
Markus Schmitt,
Yi-Ping Huang,
Petr Karpov,
Markus Heyl
Abstract:
Dynamics in correlated quantum matter is a hard problem, as its exact solution generally involves a computational effort that grows exponentially with the number of constituents. While a remarkable progress has been witnessed in recent years for one-dimensional systems, much less has been achieved for interacting quantum models in higher dimensions, since they incorporate an additional layer of co…
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Dynamics in correlated quantum matter is a hard problem, as its exact solution generally involves a computational effort that grows exponentially with the number of constituents. While a remarkable progress has been witnessed in recent years for one-dimensional systems, much less has been achieved for interacting quantum models in higher dimensions, since they incorporate an additional layer of complexity. In this work, we employ a variational method that allows for an efficient and controlled computation of the dynamics of quantum many-body systems in one and higher dimensions. The approach presented here introduces a variational class of wavefunctions based on complex networks of classical spins akin to artificial neural networks, which can be constructed in a controlled fashion. We provide a detailed prescription for such constructions and illustrate their performance by studying quantum quenches in one- and two-dimensional models. In particular, we investigate the nonequilibrium dynamics of a genuinely interacting two-dimensional lattice gauge theory, the quantum link model, for which we have recently shown -- employing the technique discussed thoroughly in this paper -- that it features disorder-free localization dynamics [P. Karpov et al., Phys. Rev. Lett. 126, 130401 (2021)]. The present work not only supplies a framework to address purely theoretical questions but also could be used to provide a theoretical description of experiments in quantum simulators, which have recently seen an increased effort targeting two-dimensional geometries. Importantly, our method can be applied to any quantum many-body system with a well-defined classical limit.
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Submitted 26 April, 2021; v1 submitted 31 July, 2020;
originally announced July 2020.
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Crystalline droplets with emergent topological color-charge in many-body systems with sign-changing interactions
Authors:
P. Karpov,
F. Piazza
Abstract:
We introduce a novel type of self-bound droplet which carries an emergent color charge. We consider a system of particles hopping on a lattice and interacting via a commensurately sign-changing potential which is attractive at a short range. The droplet formation is heralded by spontaneous crystallization into topologically distinct domains. This endows each droplet with an emergent color charge g…
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We introduce a novel type of self-bound droplet which carries an emergent color charge. We consider a system of particles hopping on a lattice and interacting via a commensurately sign-changing potential which is attractive at a short range. The droplet formation is heralded by spontaneous crystallization into topologically distinct domains. This endows each droplet with an emergent color charge governing their mutual interactions: attractive for equal colors and repulsive otherwise. The number of allowed colors is fixed only by the discrete spatial symmetries of the sign-changing part of the interaction potential. With increasing interaction range, the droplets become progressively more mobile, with their color charge still being energetically protected, allowing for nontrivial viscous dynamics of the interacting droplet plasmas formed during cooling. Sign-changing potentials with a short-range attraction appear quite naturally for light-mediated interactions and we concretely propose a realization in state-of-the-art experiments with cold atoms in a multimode optical cavity.
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Submitted 5 December, 2019; v1 submitted 30 May, 2019;
originally announced May 2019.