Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

Yaolong Zhang; Xueyao Zhou; Bin Jiang

doi:10.1021/acs.jpclett.9b00085

Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

J Phys Chem Lett. 2019 Mar 21;10(6):1185-1191. doi: 10.1021/acs.jpclett.9b00085. Epub 2019 Mar 1.

Authors

Yaolong Zhang¹, Xueyao Zhou¹, Bin Jiang¹

Affiliation

¹ Hefei National Laboratory for Physical Science at the Microscale, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes , University of Science and Technology of China , Hefei , Anhui 230026 , China.

PMID: 30802067
DOI: 10.1021/acs.jpclett.9b00085

Abstract

Direct dynamics simulations become increasingly popular in studying reaction dynamics for complex systems where analytical potential energy surfaces (PESs) are unavailable. Yet, the number and/or the propagation time of trajectories are often limited by high computational costs, and numerous energies and forces generated on-the-fly become wasted after simulations. We demonstrate here an example of reusing only a very small portion of existing direct dynamics data to reconstruct a 90-dimensional globally accurate reactive PES describing the interaction of CO₂ with a movable Ni(100) surface based on a machine learning approach. In addition to reproducing previous results with much better statistics, we predict scattering probabilities of CO₂ at the state-to-state level, which is extremely demanding for direct dynamics. We propose this unified way to investigate gaseous and gas-surface reactions of medium size, initiating with hundreds of preliminary direct dynamics trajectories, followed by low-cost and high-quality simulations on full-dimensional analytical PESs.