.png){kind=icon}
Return
Machine Learning Interatomic Potentials for Heterogeneous Catalysis
.png){kind=icon}
OA
AI
DOI：10.1002/chem.202401148.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. This review highlights the recent application of machine learning interatomic potentials (MLIPs) techniques in the atomistic modeling of heterogeneous catalytic systems. A summary and best practices for utilizing MLIPs are provided, aiming to facilitate the application of MLIPs in the catalysis community. image
Keywords:
computational chemistry
MLIPs
molecular dynamics
heterogeneous catalysis
Journal
C
IF:
3.7
Papers:
3.9W
Citations:
9.6W
Organization
Cited Papers
ShareSaveShareSaveE(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSaveThe development of nucleic acids force fields: From an unchallenged past to a competitive futureBIOPHYSICAL JOURNAL
BIOPHYSICAL JOURNALIF3.1
PREAI
ShareSaveShareSaveShareSave
View more