Dynamics of Surface Processes: Impact of Adiabatic and Nonadiabatic Energy Dissipation

Dynamics of molecular interactions with solid surfaces, such as scattering, adsorption/desorption, diffusion, and reaction, are affected by energy dissipation at surfaces. Recent progress in experimental studies of surface dynamics has stimulated intense interest in theoretical investigation of microscopic mechanisms and pathways of energy transfer. This review summarizes recent developments in modeling such processes, emphasizing new understandings of electronically adiabatic and nonadiabatic energy dissipation mechanisms and dynamics in representative systems, using various theoretical methods. In particular, machine learning has been leveraged to represent high-dimensional adiabatic potential energy surfaces, electronic friction tensors, and effective multielectron diabatic Hamiltonians. When integrated with mixed quantum-classical dynamics methods, such as molecular dynamics with electronic friction and independent electron surface hopping, these first-principles-based simulations provided unprecedented insights into the roles played by adiabatic and nonadiabatic energy dissipation channels in surface dynamics and in-depth interpretation of experimental observations.

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Publication Details

Journal
Annual Review of Physical Chemistry
Published
2025-12-05
DOI
https://doi.org/10.1146/annurev-physchem-082624-022628
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52
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article

Dynamics of Surface Processes: Impact of Adiabatic and Nonadiabatic Energy Dissipation

Yaolong Zhang, Bin Jiang, Gang Meng, Hua Guo
1 citations
Annual Review of Physical Chemistry
Machine Learning in Materials Science
0.52
article

Dynamics of Surface Processes: Impact of Adiabatic and Nonadiabatic Energy Dissipation

Yaolong Zhang, Bin Jiang, Gang Meng, Hua Guo
article en
1 citations

Abstract

Dynamics of molecular interactions with solid surfaces, such as scattering, adsorption/desorption, diffusion, and reaction, are affected by energy dissipation at surfaces. Recent progress in experimental studies of surface dynamics has stimulated intense interest in theoretical investigation of microscopic mechanisms and pathways of energy transfer. This review summarizes recent developments in modeling such processes, emphasizing new understandings of electronically adiabatic and nonadiabatic energy dissipation mechanisms and dynamics in representative systems, using various theoretical methods. In particular, machine learning has been leveraged to represent high-dimensional adiabatic potential energy surfaces, electronic friction tensors, and effective multielectron diabatic Hamiltonians. When integrated with mixed quantum-classical dynamics methods, such as molecular dynamics with electronic friction and independent electron surface hopping, these first-principles-based simulations provided unprecedented insights into the roles played by adiabatic and nonadiabatic energy dissipation channels in surface dynamics and in-depth interpretation of experimental observations.

Annual Review of Physical Chemistry
University of Science and Technology of China (CN), University of New Mexico (US)
Openalex Percentile: Top 30%
Machine Learning in Materials Science
0.52
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Dynamics of Surface Processes: Impact of Adiabatic and Nonadiabatic Energy Dissipation — Yaolong Zhang, Bin Jiang, et al. · Annual Review of Physical Chemistry (2025) | TGRS Research Map | TGRS