Learning Mappings between Equilibrium States of Liquid Systems Using Normalizing Flows

Autor(en)

Alessandro Coretti, Sebastian Falkner, Phillip Geissler, Christoph Dellago

Abstrakt

Generative models are a promising tool to address the sampling problem in multi-body and condensed-matter systems in the framework of statistical mechanics. In this work, we show that normalizing flows can be used to learn a transformation to map different liquid systems into each other allowing at the same time to obtain an unbiased equilibrium distribution through a reweighting process. Two proof-of-principles calculations are presented for the transformation between Lennard-Jones systems of particles with different depths of the potential well and for the transformation between a Lennard-Jones and a system of repulsive particles. In both numerical experiments, systems are in the liquid state. In future applications, this approach could lead to efficient methods to simulate liquid systems at ab-initio accuracy with the computational cost of less accurate models, such as force field or coarse-grained simulations.

Organisation(en)

Computergestützte Physik und Physik der Weichen Materie

Externe Organisation(en)

University of California, Berkeley

Publikationsdatum

08-2022

ÖFOS 2012

103029 Statistische Physik, 102019 Machine Learning, 103043 Computational Physics, 103036 Theoretische Physik

Schlagwörter

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/df0ddd0c-64a8-489d-9f2f-96bc3c716e81