Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy. (arXiv:2401.16487v1 [physics.chem-ph])

Extracting consistent statistics between relevant free-energy minima of a
molecular system is essential for physics, chemistry and biology. Molecular
dynamics (MD) simulations can aid in this task but are computationally
expensive, especially for systems that require quantum accuracy. To overcome
this challenge, we develop an approach combining enhanced sampling with deep
generative models and active learning of a machine learning potential (MLP). We
introduce an adaptive Markov chain Monte Carlo framework that enables the
training of one Normalizing Flow (NF) …

accuracy active learning arxiv biology boltzmann challenge chemistry consistent dynamics energy free molecular dynamics physics physics.chem-ph quantum simulations statistics systems