ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2023
Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities.
Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials.Chemical science · 2026Article
- Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics.JACS Au · 2026Article
- aims-PAX: Parallel Active Exploration Enables Expedited Construction of Machine Learning Force Fields for Molecules and Materials.Journal of chemical information and modeling · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- Harnessing Machine Learning to Enhance Transition State Search with Interatomic Potentials and Generative Models.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules.Journal of chemical information and modeling · 2025Article
- TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds.The journal of physical chemistry. A · 2025Article
- Impact of crystal structure symmetry in training datasets on GNN-based energy assessments for chemically disordered CsPbIScientific reports · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
- Fine-tuning foundation models of materials interatomic potentials with frozen transfer learning.npj computational materials · 2025Article
- Empirical Determination of Scattering Matrices from Magnetic Molecular Interferometry for Gas-Surface Collisions.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Article
- Thermal Rates and High-Temperature Tunneling from Surface Reaction Dynamics and First-Principles.Journal of the American Chemical Society · 2024Article
- A Euclidean transformer for fast and stable machine learned force fields.Nature communications · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at interfaces and the high sensitivity of dynamics to reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to accurately predict reactive sticking or desorption probabilities, as it requires averaging over tens of thousands of initial conditions. High-dimensional machine learning-based interatomic potentials are starting to be more commonly used in gas-surface dynamics, yet robust approaches to generate reliable training data and assess how model uncertainty affects the prediction of dynamic observables are not well established. Here, we employ ensemble learning to adaptively generate training data while assessing model performance with full uncertainty quantification (UQ) for reaction probabilities of hydrogen scattering on different copper facets. We use this approach to investigate the performance of two message-passing neural networks, SchNet and PaiNN. Ensemble-based UQ and iterative refinement allow us to expose the shortcomings of the invariant pairwise-distance-based feature representation in the SchNet model for gas-surface dynamics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.