1 code implementation • 31 Oct 2023 • Xinqiang Ding
By integrating configurations sampled from thermodynamic states with a prior distribution, BayesMBAR computes a posterior distribution of free energies.
no code implementations • 22 May 2022 • Xinqiang Ding, Bin Zhang
Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes.
no code implementations • 1 May 2020 • Xinqiang Ding, Bin Zhang
In this letter, we introduce a general framework for calculating the absolute free energy of a state.
1 code implementation • 12 Jun 2019 • 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.