Bridge the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surface Using Neural Networks.
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Bridge the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surface Using Neural Networks.
J Phys Chem Lett. 2019 Feb 25;:
Authors: Zhang Y, Zhou X, Jiang B
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 ninety-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 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.
PMID: 30802067 [PubMed – as supplied by publisher]