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2102.04229
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Learning the exchange-correlation functional from nature with fully differentiable density functional theory
8 February 2021
M. F. Kasim
S. Vinko
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Papers citing
"Learning the exchange-correlation functional from nature with fully differentiable density functional theory"
8 / 8 papers shown
Title
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
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NeuralSCF: Neural network self-consistent fields for density functional theory
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22 Jun 2024
Learning Pair Potentials using Differentiable Simulations
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Zhenghao Wu
Rafael Gómez-Bombarelli
84
26
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16 Sep 2022
Constants of motion network
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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
1