Modeling the Dynamics of Complex Energy Materials with Machine Learning

Materials for energy applications, e.g., heterogeneous catalysts and battery materials, often exhibit complicated chemical compositions, defects, and disorder, making the direct modeling with first principles methods challenging. Machine-learning (ML) potentials trained on first principles data enable computationally efficient linear-scaling atomistic molecular dynamic simulations with an accuracy close to the reference method. Here, I will give an overview of recent methodological advancements of ML potentials based on artificial neural networks (ANNs) [1-5] and applications of the method to challenging materials classes including metal and oxide nanoparticles and amorphous phases.

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