Year: 2022
Type: preprint
Abstract: Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastable state to another one is difficult by direct numerical methods. In view of the promises of machine learning techniques, we explore in this work two approaches to more efficiently generate transition paths: sampling methods based on generative models such as variational autoencoders, and importance sampling methods based on reinforcement learning.
Source: arXiv (Cornell University)
Institutions: Institut national de recherche en sciences et technologies du numérique, École nationale des ponts et chaussées, CERMICS, MATHERIALS: MATHematics for MatERIALS, Centre National de la Recherche Scientifique, École Polytechnique, Centre de Mathématiques Appliquées de l'École polytechnique, Université de Bordeaux, Electrophysiology and Heart Modeling Institute
Language: English
Subfield: Physical and Theoretical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
Award: G7683707404
OpenAlex ID: W4229039464