41 citations · 54 across the 3 of their papers we have counts for
3 papers
physics.chem-ph2022★ 41 cited
Contrastive Learning of Coarse-Grained Force Fields
Xinqiang Ding, Bin Zhang
Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes. Methodologies for systematic par…
cond-mat.stat-mech2020★ 7 cited
Computing Absolute Free Energy with Deep Generative Models
Xinqiang Ding, Bin Zhang
Fast and accurate evaluation of free energy has broad applications from drug design to material engineering. Computing the absolute free energy is of particular interest since it a…
stat.ML2019★ 6 cited
Learning Deep Generative Models with Annealed Importance Sampling
Xinqiang Ding, David J. Freedman
Variational inference (VI) and Markov chain Monte Carlo (MCMC) are two main approximate approaches for learning deep generative models by maximizing marginal likelihood. In this pa…