Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous Porous Palladium: A Deep Neural Network Approach
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909483701108736 |
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| author | Rodríguez, Isaías |
| author_facet | Rodríguez, Isaías |
| contents | Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on 33,310 atomic configurations from ab initio molecular dynamics simulations to model their interatomic potential. The AI-driven approach accurately predicts structural and thermal properties while significantly reducing computational costs. Validation against density functional theory confirms its reliability in reproducing forces, energies, and structural distributions. These findings highlight AI's potential in accelerating the study of amorphous materials and advancing their applications in energy and catalysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous Porous Palladium: A Deep Neural Network Approach Rodríguez, Isaías Materials Science Disordered Systems and Neural Networks Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on 33,310 atomic configurations from ab initio molecular dynamics simulations to model their interatomic potential. The AI-driven approach accurately predicts structural and thermal properties while significantly reducing computational costs. Validation against density functional theory confirms its reliability in reproducing forces, energies, and structural distributions. These findings highlight AI's potential in accelerating the study of amorphous materials and advancing their applications in energy and catalysis. |
| title | Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous Porous Palladium: A Deep Neural Network Approach |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2502.05201 |