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High-fidelity ab-initio simulations of atomistic dynamics are limited to small systems and short times, and development of surrogate machine learning models for force fields is an emerging promising direction to access long-time large-scale dynamics of complex materials systems. However, the main challenges are high accuracy, reliability, and computational efficiency of these models, which critically depend on the training data sets. We develop ML interatomic potential models that are interpretable and uncertainty-aware, and orders of magnitude faster than reference quantum methods. Principled Bayesian uncertainty quantification built into these models enables the construction of autonomous data acquisition schemes using active learning. We demonstrate on-the-fly learning of machine learning force fields and use them to gain insights into previously inaccessible physical and chemical phenomena in ion conductors, catalytic surface reactions, 2D materials phase transformations, and shape memory alloys [1,2,3].
1. J. Vandermause, et al, NPJ Computational Materials, 6, 20 (2020)
2. J. S. Lim, et al, JACS. 2020, 142, 37, 15907–15916 (2020)
3. Y. Xie et al, https://arxiv.org/abs/2008.11796