Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous
Abstract Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects. A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01629-y |
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| author | Weiqi Chen Zhiyue Xu Kang Wang Lei Gao Aisheng Song Tianbao Ma |
| author_facet | Weiqi Chen Zhiyue Xu Kang Wang Lei Gao Aisheng Song Tianbao Ma |
| author_sort | Weiqi Chen |
| collection | DOAJ |
| description | Abstract Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects. A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications. Multiphases of hydrogenated carbon materials, from crystal to amorphous, with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions. Here, we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning, and construct a general-purpose pre-trained machine learning potential (MLP) for hydrogen-carbon systems. The pre-trained MLP is further efficiently transferred to three target spaces of deposition, friction and fracture with scale reliability. This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity. |
| format | Article |
| id | doaj-art-bf3482f57cf040dcbfd5bf694936c9a2 |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-bf3482f57cf040dcbfd5bf694936c9a22025-08-20T02:11:23ZengNature Portfolionpj Computational Materials2057-39602025-05-0111111410.1038/s41524-025-01629-yTransferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphousWeiqi Chen0Zhiyue Xu1Kang Wang2Lei Gao3Aisheng Song4Tianbao Ma5State Key Laboratory of Tribology in Advanced Equipment, Tsinghua UniversityState Key Laboratory of Tribology in Advanced Equipment, Tsinghua UniversityState Key Laboratory of Tribology in Advanced Equipment, Tsinghua UniversityBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingState Key Laboratory of Tribology in Advanced Equipment, Tsinghua UniversityState Key Laboratory of Tribology in Advanced Equipment, Tsinghua UniversityAbstract Carbon materials especially with hydrogenation have attracted wide attention for their novel physical and chemical properties and broad application prospects. A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications. Multiphases of hydrogenated carbon materials, from crystal to amorphous, with covalent network and diverse chemical reactions bring huge difficulties to construct a general interatomic potential under various conditions. Here, we demonstrate a transferable active machine learning scheme with separated training of sub-feature spaces and target-oriented finetuning, and construct a general-purpose pre-trained machine learning potential (MLP) for hydrogen-carbon systems. The pre-trained MLP is further efficiently transferred to three target spaces of deposition, friction and fracture with scale reliability. This work provides a robust tool for the theoretical research of hydrogen-carbon systems and a general scheme for developing transferable MLPs in multiphase systems across compositional and conditional complexity.https://doi.org/10.1038/s41524-025-01629-y |
| spellingShingle | Weiqi Chen Zhiyue Xu Kang Wang Lei Gao Aisheng Song Tianbao Ma Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous npj Computational Materials |
| title | Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous |
| title_full | Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous |
| title_fullStr | Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous |
| title_full_unstemmed | Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous |
| title_short | Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous |
| title_sort | transferable machine learning model for multi target nanoscale simulations in hydrogen carbon system from crystal to amorphous |
| url | https://doi.org/10.1038/s41524-025-01629-y |
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