Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential

maarten — Tue, 15 Feb 2022 09:24:18 +0000

M. Cools-Ceuppens, J. Dambre, T. Verstraelen

Journal of Chemical Theory and Computation (JCTC)

18 (3), 1672–1691

2022

Abstract

Explicit-electron force fields introduce electrons or electron pairs as semiclassical particles in force fields or empirical potentials, which are suitable for molecular dynamics simulations. Even though semiclassical electrons are a drastic simplification compared to a quantum-mechanical electronic wave function, they still retain a relatively detailed electronic model compared to conventional polarizable and reactive force fields. The ability of explicit-electron models to describe chemical reactions and electronic response properties has already been demonstrated, yet the description of short-range interactions for a broad range of chemical systems remains challenging. In this work, we present the electron machine learning potential (eMLP), a new explicit electron force field in which the short-range interactions are modeled with machine learning. The electron pair particles will be located at well-defined positions, derived from localized molecular orbitals or Wannier centers, naturally imposing the correct dielectric and piezoelectric behavior of the system. The eMLP is benchmarked on two newly constructed data sets: eQM7, an extension of the QM7 data set for small molecules, and a data set for the crystalline β-glycine. It is shown that the eMLP can predict dipole moments, polarizabilities, and IR-spectra of unseen molecules with high precision. Furthermore, a variety of response properties, for example, stiffness or piezoelectric constants, can be accurately reproduced.

DOI

https://doi.org/10.1021/acs.jctc.1c00978

Private attachment

published.pdf

FWO Maarten Cools-Ceupens

wim — Thu, 05 Oct 2017 12:12:02 +0000

Project promotor

T. Verstraelen

Financierende instantie

FWO-aspirant

Project data

01/10/2017 to 30/09/2021

Mandaatmaanden

Project titel

Interacting Particle Networks: a new deep learning approach to molecular simulation of condensed phases

Personeel

M. Cools-Ceuppens

Project ref.

FWO M. Cools-Ceupens FWO3F02017004501

Maarten Cools-Ceuppens

wim — Wed, 24 Aug 2016 13:13:28 +0000

Center for Molecular Modeling - FWO M. Cools-Ceupens FWO3F02017004501