Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations

Abstract

Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive task, which limits its application to complex systems. To address this issue, machine learning (ML) models can be used as surrogates to find the ground-state charge density and reduce the computational overheads. We develop a grid-centred structural representation, based on Jacobi and Legendre polynomials combined with a linear regression, to accurately learn the converged DFT charge density. This integrates into a ML pipeline that can return any density-dependent observable, including energy and forces, at the quality of a converged DFT calculation, but at a fraction of the computational cost. Fast scanning of energy landscapes and producing starting densities for the DFT self-consistent cycle are among the applications of our scheme.

References in corpus (11)

• Quantum Spin Hall Effect and Topological Field Effect Transistor in Two-Dimensional Transition Metal Dichalcogenides
• Machine Learning Unifies the Modelling of Materials and Molecules
• On-the-fly machine learning force field generation: Application to melting points
• Opportunities and Challenges for Machine Learning in Materials Science
• Machine learning and density functional theory
• Machine learning for materials discovery: two-dimensional topological insulators
• Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C${}_{60}$
• Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
• Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
• Inversion of the chemical environment representations
• Learning electron densities in the condensed phase

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• Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys
• A general formalism for machine-learning models based on multipolar-spherical harmonics
• A charge-density machine-learning workflow for computing the infrared spectrum of molecules