High-Dimensional Atomistic Neural Network Potentials for Molecule–Surface Interactions: HCl Scattering from Au(111)
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
Ab initio molecular dynamics (AIMD) simulations of molecule–surface scattering allow first-principles characterization of the dynamics. However, the large number of density functional theory calculations along the trajectories is very costly, limiting simulations of long-time events and giving rise to poor statistics. To avoid this computational bottleneck, we report here the development of a high-dimensional molecule–surface interaction potential energy surface (PES) with movable surface atoms, using a machine learning approach. With 60 degrees of freedom, this PES allows energy transfer between the energetic impinging molecule and thermal surface atoms. Classical trajectory calculations for the scattering of DCl from Au(111) on this PES are found to agree well with AIMD simulations, with ~105-fold acceleration. Scattering of HCl from Au(111) is further investigated and compared with available experimental results.
- Research Organization:
- Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 1483849
- Journal Information:
- Journal of Physical Chemistry Letters, Journal Name: Journal of Physical Chemistry Letters Journal Issue: 3 Vol. 8; ISSN 1948-7185
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
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