Iterative-Learning Strategy for the Development of Application-Specific Atomistic Force Fields
TD Huan and R Batra and J Chapman and C Kim and A Chandrasekaran and R Ramprasad, JOURNAL OF PHYSICAL CHEMISTRY C, 123, 20715-20722 (2019).
Emerging data-driven approaches in materials science have triggered the development of numerous machine-learning force fields. In practice, they are constructed by training a statistical model on a reference database to predict potential energy and/or atomic forces. Although most of the force fields can accurately recover the properties of the training set, some of them are becoming useful for actual molecular dynamics simulations. In this work, we employ a simple iterative-learning strategy for the development of machine-learning force fields targeted at specific simulations (applications). The strategy involves (1) preparing and fingerprinting a diverse reference database of atomic configurations and forces, (2) generating a pool of machine-learning force fields by learning the reference data, (3) validating the force fields against a series of targeted applications, and (4) selectively and recursively improving the force fields that are unsuitable for a given application while keeping their performance on other applications uncompromised. We demonstrate this strategy by developing a series of machine-learning-based Al and Cu force fields that can simultaneously be used for various applications, e.g., (elastic) stress/strain analysis, stacking-fault energy calculations, and melting simulations. This strategy is also generalizable, i.e., it may be used for other materials as well.
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