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

8 February 2021

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

10 / 10 papers shown

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 39 0 0 08 Feb 2025
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory Alexander Mathiasen Hatem Helal Paul Balanca Adam Krzywaniak Ali Parviz Frederik Hvilshoj Bla.zej Banaszewski Carlo Luschi Andrew William Fitzgibbon 43 3 0 06 Feb 2024
Learning Pair Potentials using Differentiable Simulations Wujie Wang Zhenghao Wu Rafael Gómez-Bombarelli 25 23 0 16 Sep 2022
Constants of motion network M. F. Kasim Yi Heng Lim 34 4 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 18 10 0 05 May 2022
Evolving symbolic density functionals He Ma Arunachalam Narayanaswamy Patrick F. Riley Li Li 23 31 0 03 Mar 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 26 1 0 10 Feb 2022
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations Xihan Li Xiang Chen Rasul Tutunov Haitham Bou-Ammar Lei Wang Jun Wang 12 0 0 03 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 27 45 0 02 Nov 2021
DQC: a Python program package for Differentiable Quantum Chemistry M. F. Kasim S. Lehtola S. Vinko 17 34 0 22 Oct 2021