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Quantum Monte Carlo coupled with neural network wavefunctions has shown success in computing ground states of quantum many-body systems.
Monte carlo simulation of a many-fermion study
David Ceperley, Geoffrey V Chester, and Malvin H Kalos · 1977
Earlier work this paper cites.
Ground state of the electron gas by a stochastic method
David M Ceperley and Berni J Alder · 1980
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David M Ceperley · 1991
Earlier work this paper cites.
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CJ Umrigar, MP Nightingale, and KJ Runge · 1993
Earlier work this paper cites.
Theory of bose-einstein condensation in trapped gases
Franco Dalfovo, Stefano Giorgini, Lev P Pitaevskii, and Sandro Stringari · 1999
Earlier work this paper cites.
Introduction to quantum monte carlo methods applied to the electron gas
David M Ceperley · 2004
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Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Julien Toulouse, Roland Assaraf, and Cyrus J Umrigar · 2016
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Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Giuseppe Carleo and Matthias Troyer · 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Simulations of state-of-the-art fermionic neural network wave functions with diffusion monte carlo
Max Wilson, Nicholas Gao, Filip Wudarski, Eleanor Rieffel, and Norm M Tubman · 2021
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Spherical message passing for 3d molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2022
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Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, and Shuiwang Ji · 2022
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Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
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Ab-initio quantum chemistry with neural-network wavefunctions
Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo, WMC Foulkes, David Pfau, Giuseppe Carleo, and Frank Noé · 2022
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Solving many-electron schrödinger equation using deep neural networks
Jiequn Han, Linfeng Zhang, and E Weinan · 2019
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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Ab-initio solution of the many-electron schrödinger equation with deep neural networks
D. Pfau, J.S. Spencer, A.G. de G. Matthews, and W.M.C. Foulkes · 2020
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Deep-neural-network solution of the electronic schrödinger equation
Jan Hermann, Zeno Schätzle, and Frank Noé · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Efficient learning of generative models via finite-difference score matching
Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, and Jun Zhu · 2020
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Lattice convolutional networks for learning ground states of quantum many-body systems
Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, and Shuiwang Ji · 2022
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A self-attention ansatz for ab-initio quantum chemistry
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Electronic excited states in deep variational monte carlo
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Towards the ground state of molecules via diffusion monte carlo on neural networks
Weiluo Ren, Weizhong Fu, and Ji Chen · 2022
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Efficient approximations of complete interatomic potentials for crystal property prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji · 2023
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Generalizing neural wave functions
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Towards a foundation model for neural network wavefunctions
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