Learning the exchange-correlation functional from nature with fully
differentiable density functional theory

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Learning the exchange-correlation functional from nature with fully differentiable density functional theory

8 February 2021

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Papers citing "Learning the exchange-correlation functional from nature with fully differentiable density functional theory"

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Title
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory Sergei Manzhos Johann Luder Manabu Ihara Manabu Ihara 97 0 0 08 Feb 2025
NeuralSCF: Neural network self-consistent fields for density functional theory Feitong Song Ji Feng 70 1 0 22 Jun 2024
Learning Pair Potentials using Differentiable Simulations Wujie Wang Zhenghao Wu Rafael Gómez-Bombarelli 84 26 0 16 Sep 2022
Constants of motion network M. F. Kasim Yi Heng Lim 83 7 0 22 Aug 2022
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands Chenru Duan A. Ladera Julian C.-L. Liu Michael G. Taylor I. Ariyarathna Heather J. Kulik 50 11 0 05 May 2022
AD-NEGF: An End-to-End Differentiable Quantum Transport Simulator for Sensitivity Analysis and Inverse Problems Ying Zhou Xiang Chen Peng Zhang Jun Wang Lei Wang Hongfeng Guo 88 1 0 10 Feb 2022
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery Aditya Nandy Chenru Duan Heather J. Kulik AI4CE 131 47 0 02 Nov 2021
DQC: a Python program package for Differentiable Quantum Chemistry M. F. Kasim S. Lehtola S. Vinko 110 36 0 22 Oct 2021