ChIMES Carbon 2.0: A transferable machine-learned interatomic model harnessing multifidelity training data

RK Lindsey and S Bastea and S Hamel and Y Lyu and N Goldman and V Lordi, NPJ COMPUTATIONAL MATERIALS, 11, 26 (2025).

DOI: 10.1038/s41524-024-01497-y

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.

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