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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