Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session C21: Machine Learning for Quantum Matter III
3:00 PM–5:24 PM,
Monday, March 15, 2021
Sponsoring
Units:
DCOMP GDS DMP
Chair: Muratahan Aykol, Toyota Research Institute
Abstract: C21.00007 : Reactive Machine Learning Potential Models for the NO Formation Reaction*
4:36 PM–4:48 PM
Live
Presenter:
Andrew Johannesen
(University of Minnesota)
Authors:
Andrew Johannesen
(University of Minnesota)
Jason Goodpaster
(University of Minnesota)
Potential energy surfaces are sampled via molecular dynamics trajectories and extracted geometries are evaluated using DFT. Neural networks are then trained on datasets of energies, producing machine learning potential models capable of capturing disperse interactions. These models are then retrained on CCSD(T) and CASSCF derived energies to produce wavefunction level models transferable to ensembles of molecules. These models are used in Monte Carlo simulations to predict vapor-liquid equilibria and reactive equilibria and to predict second virial coefficients.
Current results include models for CO2 and N2 that reproduce DFT assessment of disperse intermolecular interactions to within 0.1kcal/mol. Future work includes producing potentials for open shell species, retraining potentials with wavefunction data, and including electrostatics.
*This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362.
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