Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
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2023Access:
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Bruno Focassio, Michelangelo Domina, Urvesh Patil, Adalberto Fazzio, Stefano Sanvito, Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations, npj Computational Materials, 9, 1, 2023Download Item:
Abstract:
As the go-to method to solve the electronic structure problem, Kohn-Sham density functional
theory (KS-DFT) can be used to obtain the ground-state charge density, total energy, and several
other key materials’ properties. Unfortunately, the solution of the Kohn-Sham equations is found
iteratively. This is a numerically intensive task, limiting the possible size and complexity of the
systems to be treated. Machine-learning (ML) models for the charge density can then be used as
surrogates to generate the converged charge density and reduce the computational cost of solving
the electronic structure problem. We derive a powerful grid-centred structural representation based
on the Jacobi and Legendre polynomials that, combined with a linear regression built on a dataefficient
workflow, can accurately learn the charge density. Then, we design a machine-learning
pipeline that can return energy and forces at the quality of a converged DFT calculation but at a
fraction of the computational cost. This can be used as a tool for the fast scanning of the energy
landscape and as a starting point to the DFT self-consistent cycle, in both cases maintaining a low
computational cost.
Author's Homepage:
http://people.tcd.ie/sanvitoshttp://people.tcd.ie/patilu
http://people.tcd.ie/dominam
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npj Computational Materials;9;
1;
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Nanoscience & MaterialsDOI:
https://doi.org/10.1038/s41524-023-01053-0https://doi.org/10.48550/arXiv.2301.13550
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