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

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...

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Bibliographic Details
Published in:Journal of chemical theory and computation 2022-03, Vol.18 (3), p.1672-1691
Main Authors: Cools-Ceuppens, Maarten, Dambre, Joni, Verstraelen, Toon
Format: Article
Language:English
Subjects:
Chemical reactions
Datasets
Dipole moments
Electrons
Glycine
Machine learning
Molecular dynamics
Molecular Mechanics
Molecular orbitals
Particle physics
Piezoelectricity
Stiffness
Wave functions
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Summary: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.
ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.1c00978