arXiv subjects

Matthias Scheffler

Publications and source records attributed to Matthias Scheffler.

At least 19 recordsLinked to original sources

arXiv2026

Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials

Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, perturbative treatments of electron-phonon interactions and the harmonic phonon picture often break down, necessitating non-perturbative approaches. The ab initio Kubo-Greenwood(aiKG) formalism provides a rigorous framework for evaluating temperature-dependent carrier transport beyond the harmonic approximation. Nevertheless, its practical application is computationally demanding because it requires large supercells, extensive statistical sampling, and extrapolation to the zero-frequency limit. In this work, we introduce an artificial-intelligence(AI)-assisted aiKG framework that incorporates the deep-learning Hamiltonian model. By predicting the Kohn-Sham Hamiltonian with sub-meV accuracy for supercells of up to 250 atoms, the model bypasses the costly iterative self-consistent field calculations while retaining first-principles reliability within the scope of effects captured by the training data. Using a strongly anharmonic thermal insulator, potassium iodide(KI) as a benchmark system, we demonstrate that the proposed approach enables efficient simulations of electronic structure and transport properties from a large supercell. The framework reproduces temperature-dependent carrier mobilities, spectral functions, and effective masses in close agreement with the underlying density functional theory while reducing computational cost to 10%. These results suggest that the AI-assisted aiKG framework can make non-perturbative transport calculations tractable for strongly anharmonic materials, opening a scalable route towards realistic simulations and accelerated discovery of new functional materials.

cond-mat.mtrl-sci↗

arXiv2026

Unveiling the Core of Materials Properties via SISSO and Sensitivity Analysis

Interpretable AI can reveal physical principles governing intricate materials properties by uncovering explicit relationships between physical parameters and target properties. The sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach identifies analytical expressions that correlate a target property with a small set of parameters, termed materials genes, selected from a large pool of candidates. However, multiple gene combinations can yield equally accurate SISSO models, with individual genes contributing with different weights. Here, we establish a derivative-based sensitivity analysis that resolves the non-uniqueness of symbolic-regression descriptions, enhances interpretability, thereby enabling deeper physical insight. This analysis reveals how distinct gene combinations encode equivalent information and identifies valence orbital radii, nuclear charges, and their products as the key quantities governing the equilibrium lattice constant of perovskites.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's translational symmetry. In this work, we present an efficient band unfolding method for atomic orbital (AO) basis sets that explicitly accounts for both the non-orthogonality of atomic orbitals and their atom-centered nature. Unlike existing approaches that typically rely on a plane-wave representation of the (semi-)valence states, we here derive analytical expressions that recasts the primitive cell translational operator and the associated Bloch-functions in the supercell AO basis. In turn, this enables the accurate and efficient unfolding of conduction, valence, and core states in all-electron codes, as demonstrated by our implementation in the all-electron ab initio simulation package FHI-aims, which employs numeric atom-centered orbitals. We explicitly demonstrate the capability of running large-scale unfolding calculations for systems with thousands of atoms and showcase the importance of this technique for computing temperature-dependent spectral functions in strongly anharmonic materials using CuI as example.

cond-mat.mtrl-sci↗

arXiv2025

Switchable polarization in non-ferroelectric SrTiO$_3$

Perovskites with tunable and switchable polarization hold immense promise for unlocking novel functionalities. Using density-functional theory, we reveal that intrinsic defects can induce, enhance, and control polarization in non-ferroelectric perovskites, with SrTiO$_3$ as our model system. At high defect concentrations, these systems exhibit strong spontaneous polarization - comparable to that of conventional ferroelectrics. Crucially, this polarization is switchable, enabled by the inherent symmetry-equivalence of defect sites in SrTiO$_3$. Strikingly, polarization switching not only reverses the polarization direction and modulates its magnitude but also modifies the spatial distribution of localized defect states. This dynamic behavior points to unprecedented responses to external stimuli, opening new avenues for defect-engineered materials design.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Computation of the Long-Range Exact Exchange using an Extended Screening Function

We introduce a computationally efficient screening for the Coulomb potential that also allows calculating approximated long-range exact exchange contributions with an accuracy similar to an explicit full-range evaluation of the exact exchange. Starting from the screening function of the HSE functional, i.e., the complementary error function, as zeroth order, a first-order Taylor expansion in terms of the screening parameter {\omega} is proposed as an approximation of the long-range Coulomb potential. The resulting extended screening function has a similar spatial extend as the complementary error function leading to a computational speed comparable to screened hybrid functionals such as HSE06, but with long-range exact exchange contributions included. The approach is tested and demonstrated for prototypical semiconductors and organic crystals using the PBE0 functional. Predicted energy band gaps, total energies, cohesive energies, and lattice energies from the first-order approximated PBE0 functional are close to those from the unmodified PBE0 functional, but are obtained at significantly reduced computational cost.

cond-mat.mtrl-sci↗

arXiv2025

Materials Database from All-electron Hybrid Functional DFT Calculations

Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient approximation (GGA) for electron exchange and correlation. This limits the reliability of these databases, as well as the artificial intelligence (AI) models trained on them, for certain classes of materials and properties which are not well described by GGA. In this paper, we describe a database of 7,024 inorganic materials presenting diverse structures and compositions generated using hybrid functional calculations enabled by their efficient implementation in the all-electron code FHI-aims. The database is used to evaluate the thermodynamic and electrochemical stability of oxides relevant to catalysis and energy related applications. We illustrate how the database can be used to train AI models for material properties using the sure-independence screening and sparsifying operator (SISSO) approach.

cond-mat.mtrl-sci↗

arXiv2025

A high-performance and portable implementation of the SISSO method for CPUs and GPUs

SISSO (sure-independence screening and sparsifying operator) is an artificial intelligence (AI) method based on symbolic regression and compressed sensing widely used in materials science research. SISSO++ is its C++ implementation that employs MPI and OpenMP for parallelization, rendering it well-suited for high-performance computing (HPC) environments. As heterogeneous hardware becomes mainstream in the HPC and AI fields, we chose to port the SISSO++ code to GPUs using the Kokkos performance-portable library. Kokkos allows us to maintain a single codebase for both Nvidia and AMD GPUs, significantly reducing the maintenance effort. In this work, we summarize the necessary code changes we did to achieve hardware and performance portability. This is accompanied by performance benchmarks on Nvidia and AMD GPUs. We demonstrate the speedups obtained from using GPUs across the three most time-consuming parts of our code.

cs.PF↗

arXiv2025

Exploring the accuracy of the equation-of-motion coupled-cluster band gap of solids

While the periodic equation-of-motion coupled-cluster (EOM-CC) method promises systematic improvement of electronic band gap calculations in solids, its practical application at the singles and doubles level (EOM-CCSD) is hindered by severe finite-size errors in feasible simulation cells. We present a hybrid approach combining EOM-CCSD with the computationally efficient $GW$ approximation to estimate thermodynamic limit band gaps for several insulators and semiconductors. Our method substantially reduces required cell sizes while maintaining accuracy. Comparisons with experimental gaps and self-consistent $GW$ calculations reveal that deviations in EOM-CCSD predictions correlate with reduced single excitation character of the excited many-electron states. Our work not only provides a computationally tractable approach to EOM-CC calculations in solids but also reveals fundamental insights into the role of single excitations in electronic-structure theory.

cond-mat.mtrl-sci↗

arXiv2025

Density-Functional Perturbation Theory with Numeric Atom-Centered Orbitals

This paper represents one contribution to a larger Roadmap article reviewing the current status of the FHI-aims code. In this contribution, the implementation of density-functional perturbation theory in a numerical atom-centered framework is summarized. Guidelines on usage and links to tutorials are provided.

cond-mat.mtrl-sci↗

arXiv2024

Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.

cond-mat.mtrl-sci↗

arXiv2024

Temperature-dependent Electronic Spectral Functions from Band-Structure Unfolding

The electronic band structure, describing the periodic dependence of electronic quantum states on lattice momentum in reciprocal space, is a fundamental concept in solid-state physics. However, it's only well-defined for static nuclei. To account for thermodynamic effects, this concept must be generalized by introducing the temperature-dependent spectral function, which characterizes the finite-width distributions of electronic quantum states at each reciprocal vector. Many-body perturbation theory can compute spectral functions and associated observables, but it approximates the dynamics of nuclei and its coupling to the electrons using the harmonic approximation and linear-order electron-phonon coupling elements, respectively. These approximations may fail at elevated temperatures or for mobile atoms. To avoid inaccuracies, the electronic spectral function can be obtained non-perturbatively, capturing higher-order couplings between electrons and vibrational degrees of freedom. This process involves recovering the representation of supercell bands in the first Brillouin zone of the primitive cell, a process known as unfolding. In this contribution, we describe the implementation of the band-structure unfolding technique in the electronic-structure theory package FHI-aims and the updates made since its original development.

cond-mat.mtrl-sci↗

arXiv2024

Coupled-cluster theory for the ground state and for excitations

In the molecular quantum chemistry community, coupled-cluster (CC) methods are well-recognized for their systematic convergence and reliability. The extension of the theory to extended systems has been comparably recent, so that developments and studies of periodic CC methods for both the ground-state and for excited states are still active fields of research and provide valuable benchmark data when the reliability of density functional approximations is questionable. In this contribution we describe the CC-aims interface between the FHI-aims and the Cc4s software packages. This linkage makes a variety of correlated wave function-based ground-state methods including M\o ller-Plesset perturbation theory (MP2), the random-phase approximation (RPA) and the gold-standard of quantum chemistry CCSD(T) method for both molecular and periodic applications accessible. This contribution discusses these ground-state methods for clusters and molecules, as well as for periodic systems. In particular, we discuss recent advancements and the implementation of the equation-of-motion CC method for the calculation of ionization (IP-EOM-CCSD) and electron attachment (EA-EOM-CCSD) processes. Open questions and routes to solutions are discussed as well.

physics.chem-ph↗

arXiv2024

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning

Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By training these potentials on data generated from ab initio methods, their averaged predictions can exhibit comparable performance to ab initio methods at a fraction of the cost. However, insufficient training sets might lead to an improper description of the dynamics in strongly anharmonic materials, because critical effects might be overlooked in relevant cases, or only incorrectly captured, or hallucinated by the MLIP when they are not actually present. In this work, we show that an active learning scheme that combines MD with MLIPs (MLIP-MD) and uncertainty estimates can avoid such problematic predictions. In short, efficient MLIP-MD is used to explore configuration space quickly, whereby an acquisition function based on uncertainty estimates and on energetic viability is employed to maximize the value of the newly generated data and to focus on the most unfamiliar but reasonably accessible regions of phase space. To verify our methodology, we screen over 112 materials and identify 10 examples experiencing the aforementioned problems. Using CuI and AgGaSe$_2$ as archetypes for these problematic materials, we discuss the physical implications for strongly anharmonic effects and demonstrate how the developed active learning scheme can address these issues.

cond-mat.mtrl-sci↗

arXiv2024

Finite-size Effects in periodic EOM-CCSD for Ionization Energies and Electron Affinities: Convergence Rate and Extrapolation to the Thermodynamic Limit

We investigate the convergence of quasi-particle energies for periodic systems to the thermodynamic limit using increasingly large simulation cells corresponding to increasingly dense integration meshes in reciprocal space. The quasi-particle energies are computed at the level of equation-of-motion coupled-cluster theory for ionization (IP-EOM-CC) and electron attachment processes (EA-EOM-CC). By introducing an electronic correlation structure factor, the expected asymptotic convergence rates for systems with different dimensionality are formally derived. We rigorously test these derivations through numerical simulations for trans-Polyacetylene using IP/EA-EOM-CCSD and the G0W0@HF approximation, which confirm the predicted convergence behavior. Our findings provide a solid foundation for efficient schemes to correct finite-size errors in IP/EA-EOM-CCSD calculations.

cond-mat.mtrl-sci↗

arXiv2024

Carrier Mobility of Strongly Anharmonic Materials from First Principles

First-principle approaches for phonon-limited electronic transport are typically based on many-body perturbation theory and transport equations. With that, they rely on the validity of the quasi-particle picture for electrons and phonons, which is known to fail in strongly anharmonic systems. In this work, we demonstrated the relevance of effects beyond the quasi-particle picture by combining ab initio molecular dynamics and the Kubo-Greenwood (KG) formalism to establish a non-perturbative, stochastic method to calculate carrier mobilities while accounting for all orders of anharmonic and electron-vibrational couplings. In particular, we propose and exploit several numerical strategies that overcome the notoriously slow convergence of the KG formalism for both electronic and nuclear degree of freedom in crystalline solids. The capability of this method is demonstrated by calculating the temperature-dependent electron mobility of the strongly anharmonic oxide perovskites SrTiO3 and BaTiO3 across a wide range of temperatures. We show that the temperature-dependence of the mobility is largely driven by anharmonic, higher-order coupling effects and rationalize these trends in terms of the non-perturbative electronic spectral functions.

cond-mat.mtrl-sci↗

arXiv2024

Coherent Collections of Rules Describing Exceptional Materials Identified with a Multi-Objective Optimization of Subgroups

Useful materials are often statistically exceptional and they might be overlooked by AI models that attempt to describe all materials simultaneously. These global models perform well for the majority of (useless) materials, but they do not necessarily capture the useful ones. Subgroup discovery (SGD) identifies rules describing subsets of materials (SGs) associated to exceptional values, e.g., high values, of a materials property of interest. Thus, SGD can better capture exceptional materials compared to most widely used AI techniques. Previous works focused on the SG that maximizes an objective function that establishes one tradeoff between the size of the SG and the exceptionality of the distribution of property values in the SG. However, this optimization does not give a unique solution, but many SGs typically have similar objective-function values. Here, we identify a Pareto region of SGs presenting a multitude of size-exceptionality tradeoffs. The approach is demonstrated by the learning of rules describing perovskites with high bulk modulus. These rules are used to screen a large space of perovskites and to efficiently identify materials with bulk modulus up to 13 % higher than the highest value of the training set.

cond-mat.mtrl-sci↗

arXiv2024

Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms

Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance CPU compute clusters. The introduction of several new refined Message Passing Interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA based simulations for a broad range of chemical systems, including hybrid organic-inorganic perovskites, organic crystals and ice crystals with up to 30,576 atoms (101,920 electrons described by 244,608 basis functions).

cond-mat.mtrl-sci↗

arXiv2024

Roadmap on Data-Centric Materials Science

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.

cond-mat.mtrl-sci↗