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Data-Driven Improvement of Local Hybrid Functionals: Neural-Network-Based Local Mixing Functions and Power-Series Correlation Functionals.
J Chem Theory Comput
January 2025
Technische Universitát Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7, Straße des 17. Juni 135, Berlin D-10623, Germany.
Local hybrid functionals (LHs) use a real-space position-dependent admixture of exact exchange (EXX), governed by a local mixing function (LMF). The systematic construction of LMFs has been hampered over the years by a lack of exact physical constraints on their valence behavior. Here, we exploit a data-driven approach and train a new type of "n-LMF" as a relatively shallow neural network.
View Article and Find Full Text PDFLattice Multislice Algorithm for Fast Simulation of Scanning Transmission Electron Microscopy Images.
Microsc Microanal
January 2025
Department of Mathematics, University of South Carolina, 1523 Greene St, Columbia, SC 29208, USA.
We introduce a new approach to the numerical simulation of Scanning Transmission Electron Microscopy images. The Lattice Multislice Algorithm takes advantage of the fact that the electron waves passing through the specimen have limited bandwidth and therefore can be approximated very well by a low-dimensional linear space spanned by translations of a well-localized function. Just like in the PRISM algorithm recently published by C.
View Article and Find Full Text PDFSpatial Signatures of Electron Correlation in Least-Squares Tensor Hypercontraction.
J Phys Chem A
January 2025
Department of Chemistry, Southern Methodist University, Dallas, Texas 75275, United States.
Least-squares tensor hypercontraction (LS-THC) has received some attention in recent years as an approach to reduce the significant computational costs of wave function-based methods in quantum chemistry. However, previous work has demonstrated that LS-THC factorization performs disproportionately worse in the description of wave function components (e.g.
View Article and Find Full Text PDFEfficient Sampling for Machine Learning Electron Density and Its Response in Real Space.
J Chem Theory Comput
January 2025
Key Laboratory of Precision and Intelligent Chemistry, Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China.
Electron density is a fundamental quantity that can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for electron density based on either an atom-centered basis or a real-space grid have been proposed, the demand for a number of high-order basis functions or grid points is enormous. In this work, we propose an efficient grid-point sampling strategy that combines targeted sampling favoring a large density and a screening of grid points associated with linearly independent atomic features.
View Article and Find Full Text PDFSpectral operator representations.
NPJ Comput Mater
December 2024
Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
Machine learning in atomistic materials science has grown to become a powerful tool, with most approaches focusing on atomic geometry, typically decomposed into local atomic environments. This approach, while well-suited for machine-learned interatomic potentials, is conceptually at odds with learning complex intrinsic properties of materials, often driven by spectral properties commonly represented in reciprocal space (e.g.
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