ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2024
Toward an ab Initio Description of Adsorbate Surface Dynamics.
Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Automated Pynta-Based Curriculum for ML-Accelerated Calculation of Transition States.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
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Authors and funding
2 authors.
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Abstract
The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future.
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Registered trials
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