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-fide...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.展开更多
基金supported by LLNL LDRD 23-SI-006.LLNL-JRNL-861515.
文摘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.
