Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM+][OAc-]

Stoppelman, John P.; McDaniel, Jesse G.

doi:10.1063/5.0063187

Title: Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM⁺][OAc^-]

Journal Article · 13 September 2021 · Journal of Chemical Physics

DOI: https://doi.org/10.1063/5.0063187 · OSTI ID:1814926

^[1];

^[2]

Georgia Inst. of Technology, Atlanta, GA (United States); Georgia Tech
Georgia Inst. of Technology, Atlanta, GA (United States)

Reactive molecular dynamics simulations enable detailed understanding of solvent effects on chemical reaction mechanisms and reaction rates. While classical molecular dynamics using reactive force fields allows significantly longer simulation time scales and larger system sizes compared with ab initio molecular dynamics, constructing reactive force fields is a difficult and complex task. In this work, we describe a general approach following the Empirical Valence Bond (EVB) framework for constructing ab initio reactive force fields for condensed phase simulations by combining physics-based methods with neural networks (PB/NN). The physics-based terms ensure correct asymptotic behavior of electrostatic, polarization, and dispersion interactions, and are compatible with existing solvent force fields. Neural networks are utilized for versatile description of short-range orbital interactions within the transition state region, and accurate rendering of vibrational motion of the reacting complex. Herein, we demonstrate our methodology for a simple deprotonation reaction of the 1-ethyl-3-methylimidazolium (EMIM+) cation with acetate to form 1-ethyl- 3-methylimidazol-2-ylidene and acetic acid. Our PB/NN force field exhibits ~ 1 kJ/mol MAE accuracy within the transition state region for the gas-phase complex. To characterize solvent modulation of the reaction profile, we compute potentials of mean force (PMFs) for the gas-phase reaction as well as the reaction within a four ion cluster, and benchmark against ab initio molecular dynamics simulations. We find that the surrounding ionic environment significantly destabilizes formation of the carbene product, and we show that this effect is accurately captured by the reactive force field. By construction, the PB/NN potential may be directly employed for simulations of other solvents/chemical environments without additional parameterization.

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Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: SC0020279

OSTI ID:: 1814926

Journal Information:: Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 10 Vol. 155; ISSN 0021-9606

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM+][OAc-]

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Title: Physics-based, neural network force fields for reactive molecular dynamics: Investigation of carbene formation from [EMIM⁺][OAc^-]