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1篇 您的检索式：作者名="Urvesh Patil"

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		题名	作者	年代	出处	被引量
	1	Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations显示文摘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.	Bruno Focassio Michelangelo Domina Urvesh Patil Adalberto Fazzio Stefano Sanvito	2023	npj Computational Materials2023，，1：	0

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