This repository provides resources and workflows for modeling Pt-functionalized graphene using machine learning interatomic potentials (MLIPs), focusing on the nucleation and growth of Pt on graphene and its hydrogen reactivity for applications in sensing and storage.
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Equivariant Neural Network Potential (NNP):
- Trained and deployed for molecular dynamics (MD) annealing and minima hopping simulations.
- Enables the prediction of Pt/graphene crystal structures at tens of nanometer scales under varying Pt loadings.
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Nucleation and Growth Dynamics:
- Analyzed the behavior of Pt atoms on graphene, including their nucleation and growth patterns.
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Hydrogen Reactivity Modeling:
- Assessed hydrogen capture efficiency, dissociation, and recombination rates on optimized Pt/graphene structures.
- Identified optimal Pt loadings for hydrogen sensing and storage.
More will be released soon!!