Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface.

ABSTRACT: Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on

SUBMITTER: Shahbazi F 

PROVIDER: S-EPMC11372171 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature