180 citations · 526 across the 7 of their papers we have counts for
7 papers
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
Xiang Fu, Zhenghao Wu, Wujie Wang +4
Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…
Learning Pair Potentials using Differentiable Simulations
Wujie Wang, Zhenghao Wu, Rafael Gómez-Bombarelli
Learning pair interactions from experimental or simulation data is of great interest for molecular simulations. We propose a general stochastic method for learning pair interaction…
Generative Coarse-Graining of Molecular Conformations
Wujie Wang, Minkai Xu, Chen Cai +5
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, s…
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu, Wujie Wang, Shitong Luo +4
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two ste…
Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda +3
Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a p…
Differentiable Molecular Simulations for Control and Learning
Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli
Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired t…
Coarse-Graining Auto-Encoders for Molecular Dynamics
Wujie Wang, Rafael Gómez-Bombarelli
Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of n…