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

作者：Rebecca Lindsey, Sorin Bastea, Sébastien Hamel, Yanjun Lyu, Nir Goldman, Vincenzo Lordi · 发表于：npj Computational Materials · 年份：2025 · DOI：10.1038/s41524-024-01497-y · 被引用次数：11 · 研究领域：Machine Learning in Materials Science、High-pressure geophysics and materials、Diamond and Carbon-based Materials Research

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.