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Found 4 papers, 2 papers with code
Bayesian Multistate Bennett Acceptance Ratio Methods
By integrating configurations sampled from thermodynamic states with a prior distribution, BayesMBAR computes a posterior distribution of free energies.
Contrastive Learning of Coarse-Grained Force Fields
Coarse-grained models have proven helpful for simulating complex systems over long timescales to provide molecular insights into various processes.
Computing Absolute Free Energy with Deep Generative Models
In this letter, we introduce a general framework for calculating the absolute free energy of a state.
Learning Deep Generative Models with Annealed Importance Sampling
Variational inference (VI) and Markov chain Monte Carlo (MCMC) are two main approximate approaches for learning deep generative models by maximizing marginal likelihood.
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