Deep Variational Free Energy Approach to Dense Hydrogen

13 September 2022

Lei Wang

Papers citing "Deep Variational Free Energy Approach to Dense Hydrogen"

25 / 25 papers shown

Title
Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching Hao Xie Saburo Howard Guglielmo Mazzola 67 2 0 17 Jan 2025
Discovering Quantum Phase Transitions with Fermionic Neural Networks G. Cassella H. Sutterud Sam Azadi N. Drummond David Pfau J. Spencer W. Foulkes 33 46 0 10 Feb 2022
$$m^\ast$ of two-dimensional electron gas: a neural canonical transformation study$ $m^\ast$ of two-dimensional electron gas: a neural canonical transformation study H.-j. Xie Linfeng Zhang Lei Wang 54 8 0 10 Jan 2022
High pressure hydrogen by machine learning and quantum Monte Carlo Andrea Tirelli Giacomo Tenti K. Nakano S. Sorella 47 20 0 21 Dec 2021
Normalizing flows for atomic solids Peter Wirnsberger George Papamakarios Borja Ibarz S. Racanière Andy Ballard Alexander Pritzel Charles Blundell 48 40 0 16 Nov 2021
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions Nicholas Gao Stephan Günnemann 56 38 0 11 Oct 2021
Ab-initio study of interacting fermions at finite temperature with neural canonical transformation Hao Xie Linfeng Zhang Lei Wang 51 26 0 18 May 2021
Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks Michael Scherbela Rafael Reisenhofer Leon Gerard P. Marquetand Philipp Grohs 71 49 0 18 May 2021
Scalable Normalizing Flows for Permutation Invariant Densities Marin Bilos Stephan Günnemann TPM 31 24 0 07 Oct 2020
Exchangeable Neural ODE for Set Modeling Yang Li Haidong Yi Christopher M. Bender Siyuan Shan Junier B. Oliva BDL 41 30 0 06 Aug 2020
Equivariant flow-based sampling for lattice gauge theory G. Kanwar M. S. Albergo D. Boyda Kyle Cranmer D. Hackett S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 45 175 0 13 Mar 2020
Targeted free energy estimation via learned mappings Peter Wirnsberger A. J. Ballard George Papamakarios Stuart Abercrombie S. Racanière Alexander Pritzel Danilo Jimenez Rezende Charles Blundell 53 89 0 12 Feb 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 195 1,691 0 05 Dec 2019
Equivariant Flows: sampling configurations for multi-body systems with symmetric energies Jonas Köhler Leon Klein Frank Noé 73 91 0 02 Oct 2019
Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks David Pfau J. Spencer A. G. Matthews W. Foulkes 72 461 0 05 Sep 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 67 408 0 25 Jun 2019
Flow-based generative models for Markov chain Monte Carlo in lattice field theory M. S. Albergo G. Kanwar P. Shanahan AI4CE 46 218 0 26 Apr 2019
Solving Statistical Mechanics Using Variational Autoregressive Networks Dian Wu Lei Wang Pan Zhang 61 185 0 27 Sep 2018
Monge-Ampère Flow for Generative Modeling Linfeng Zhang E. Weinan Lei Wang DRL 75 63 0 26 Sep 2018
Neural Network Renormalization Group Shuo-Hui Li Lei Wang BDL DRL 59 125 0 08 Feb 2018
Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics Linfeng Zhang Jiequn Han Han Wang R. Car E. Weinan 64 1,147 0 30 Jul 2017
On Markov chain Monte Carlo methods for tall data Rémi Bardenet Arnaud Doucet Chris Holmes 71 279 0 11 May 2015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature James Martens Roger C. Grosse ODL 95 1,009 0 19 Mar 2015
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 154 2,796 0 20 Feb 2015
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 248 2,621 0 29 Jun 2012