Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy

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 th...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-04
Main Authors: Molina-Taborda, Ana, Cossio, Pilar, Lopez-Acevedo, Olga, Gabrié, Marylou
Format: Article
Language:English
Subjects:
Accuracy
Active learning
Boltzmann distribution
Configurations
Density functional theory
Energy consumption
Free energy
Isomerization
Iterative methods
Machine learning
Markov chains
Molecular dynamics
Nanoclusters
Normalizing (statistics)
Quantum mechanics
Samplers
Sampling
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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) and one MLP per state, achieving rapid convergence towards the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free-energy differences or optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and catalysis.
ISSN:2331-8422