ChIMES Carbon 2.0: A Transferable Machine-Learned Interatomic Model Harnessing Multifidelity Training Data

26 March 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


We present a new parameterization 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 model shows 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. Model applications to carbon melting point prediction, shockwave-driven con- version of graphite to diamond, and thermal conversion of nanodiamond to graphitic nanoonion are provided. Ultimately, we find our new model to be robust, accurate, and well-suited for modeling evolution in carbon systems under extreme conditions.


machine learning
interatomic model


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