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High-Dimensional Atomistic Neural Network Potentials for Molecule-Surface Interactions: HCl Scattering from Au(111).

Brian Kolb^1,2, Xuan Luo³, Xueyao Zhou³

¹Department of Chemistry and Chemical Biology, University of New Mexico , Albuquerque, New Mexico 87131, United States.

The Journal of Physical Chemistry Letters

|January 20, 2017

Summary

Machine learning accelerates molecule-surface scattering simulations by creating a high-dimensional potential energy surface (PES). This method enables accurate dynamics studies, offering a 10⁵-fold speedup over traditional ab initio molecular dynamics (AIMD).

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Area of Science:

Computational Chemistry
Surface Science
Materials Science

Background:

Ab initio molecular dynamics (AIMD) simulations offer first-principles insights into molecule-surface scattering dynamics.
AIMD simulations are computationally expensive due to extensive density functional theory calculations, limiting simulation time and statistical accuracy.

Purpose of the Study:

To develop a computationally efficient, high-dimensional potential energy surface (PES) for molecule-surface interactions.
To enable accurate simulation of energy transfer between molecules and movable surface atoms.

Main Methods:

Employed a machine learning approach to construct a high-dimensional PES incorporating 60 degrees of freedom.
Utilized classical trajectory calculations on the developed PES for scattering simulations.
Validated the PES by comparing DCl scattering on Au(111) with AIMD simulations.

Main Results:

The machine learning-based PES accurately reproduces AIMD simulation results for DCl/Au(111) scattering.
Achieved a computational acceleration of approximately 10⁵-fold compared to traditional AIMD.
Investigated HCl scattering on Au(111), showing good agreement with experimental data.

Conclusions:

The developed high-dimensional PES significantly overcomes the computational bottleneck of AIMD for molecule-surface dynamics.
This machine learning approach provides a powerful tool for accurate and accelerated simulations of molecule-surface scattering.
The method facilitates detailed investigation of surface dynamics and energy transfer processes.