Machine Learning Interatomic Potentials for Heterogeneous Catalysis
Abstract: Atomistic modeling can provide valuable insights into the design of novel heterogeneous catalysts as needed nowadays in the areas of, e. g., chemistry, materials science, and biology. Classical force fields and ab initio calculations have been widely adopted in molecular simulations. However, these methods usually suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of heterogeneous catalytic systems. We also highlight the best practices and challenges for MLIPs and give an outlook for future works on MLIPs in the field of heterogeneous catalysis.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Machine Learning Interatomic Potentials for Heterogeneous Catalysis ; day:16 ; month:10 ; year:2024 ; extent:18
Chemistry - a European journal ; (16.10.2024) (gesamt 18)
- Creator
-
Tang, Deqi
Ketkaew, Rangsiman
Luber, Sandra
- DOI
-
10.1002/chem.202401148
- URN
-
urn:nbn:de:101:1-2410171414256.412412409440
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Tang, Deqi
- Ketkaew, Rangsiman
- Luber, Sandra
Other Objects (12)
Cartesian atomic cluster expansion for machine learning interatomic potentials
Discrepancies and error evaluation metrics for machine learning interatomic potentials
An accurate and transferable machine learning interatomic potential for nickel
Interatomic potentials: achievements and challenges
Optimal interatomic potentials using modified method of least squares: Optimal form of interatomic potentials
Machine learned interatomic potentials for ternary carbides trained on the AFLOW database
Transferability of interatomic potentials for silicene
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning
A machine-learned interatomic potential for silica and its relation to empirical models
Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning
Cartesian atomic cluster expansion for machine learning interatomic potentials
Discrepancies and error evaluation metrics for machine learning interatomic potentials
An accurate and transferable machine learning interatomic potential for nickel
Interatomic potentials: achievements and challenges
Optimal interatomic potentials using modified method of least squares: Optimal form of interatomic potentials
Machine learned interatomic potentials for ternary carbides trained on the AFLOW database
Transferability of interatomic potentials for silicene
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning
A machine-learned interatomic potential for silica and its relation to empirical models
Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning
Cartesian atomic cluster expansion for machine learning interatomic potentials
Discrepancies and error evaluation metrics for machine learning interatomic potentials
An accurate and transferable machine learning interatomic potential for nickel
Interatomic potentials: achievements and challenges
Optimal interatomic potentials using modified method of least squares: Optimal form of interatomic potentials
Machine learned interatomic potentials for ternary carbides trained on the AFLOW database
Transferability of interatomic potentials for silicene
Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials
Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning
A machine-learned interatomic potential for silica and its relation to empirical models