ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data
Abstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. The resulting models show significant impr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-024-01497-y |
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| author | Rebecca K. Lindsey Sorin Bastea Sebastien Hamel Yanjun Lyu Nir Goldman Vincenzo Lordi |
| author_facet | Rebecca K. Lindsey Sorin Bastea Sebastien Hamel Yanjun Lyu Nir Goldman Vincenzo Lordi |
| author_sort | Rebecca K. Lindsey |
| collection | DOAJ |
| description | Abstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets. Applications to carbon melting point prediction, shockwave-driven conversion of graphite to diamond, and thermal conversion of nanodiamond to graphitic nanoonion are provided. Ultimately, we find the new models to be robust, accurate, and well-suited for modeling evolution in carbon systems under extreme conditions. |
| format | Article |
| id | doaj-art-db46dbc2bb9b4fcab99ecfdb57bf9f88 |
| institution | Kabale University |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-db46dbc2bb9b4fcab99ecfdb57bf9f882025-02-09T12:46:41ZengNature Portfolionpj Computational Materials2057-39602025-02-0111111310.1038/s41524-024-01497-yChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training dataRebecca K. Lindsey0Sorin Bastea1Sebastien Hamel2Yanjun Lyu3Nir Goldman4Vincenzo Lordi5Department of Chemical Engineering, University of MichiganPhysical and Life Sciences Directorate, Lawrence Livermore National LaboratoryPhysical and Life Sciences Directorate, Lawrence Livermore National LaboratoryDepartment of Materials Science and Engineering, University of MichiganPhysical and Life Sciences Directorate, Lawrence Livermore National LaboratoryPhysical and Life Sciences Directorate, Lawrence Livermore National LaboratoryAbstract We present new parameterizations of the ChIMES physics informed machine-learned interatomic model for simulating carbon under conditions ranging from 300 K and 0 GPa to 10,000 K and 100 GPa, along with a new multi-fidelity active learning strategy. The resulting models show significant improvement in accuracy and temperature/pressure transferability relative to the original ChIMES carbon model developed in 2017 and can serve as a foundation for future transfer-learned ChIMES parameter sets. Applications to carbon melting point prediction, shockwave-driven conversion of graphite to diamond, and thermal conversion of nanodiamond to graphitic nanoonion are provided. Ultimately, we find the new models to be robust, accurate, and well-suited for modeling evolution in carbon systems under extreme conditions.https://doi.org/10.1038/s41524-024-01497-y |
| spellingShingle | Rebecca K. Lindsey Sorin Bastea Sebastien Hamel Yanjun Lyu Nir Goldman Vincenzo Lordi ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data npj Computational Materials |
| title | ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data |
| title_full | ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data |
| title_fullStr | ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data |
| title_full_unstemmed | ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data |
| title_short | ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data |
| title_sort | chimes carbon 2 0 a transferable machine learned interatomic model harnessing multifidelity training data |
| url | https://doi.org/10.1038/s41524-024-01497-y |
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