An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2

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Deep Potentials for Materials Science (2022, background): “Behler and Parrinello (BP) [18] introduced ML neural network potential (NNP) approach in which radial and angular symmetry functions are used as atomic environment descriptors; NNP has found applications in bulk silicon [18], carbon [19], TiO2 [20] and many other materials [21].”
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python (2019, background): “To address these issues, several projects developed adapters (Artrith & Urban, 2016; Artrith et al., 2017; Lot et al., 2019; Onat et al., 2018) between common ML languages, like Torch and Tensorflow, and common MD languages like LAMMPS.”
Machine Learning a General-Purpose Interatomic Potential for Silicon (2018, background): “Modelling the short range interactions with artificial neural networks (NN) really took off about a decade ago37, starting with the bulk phases of silicon38,39, with many more to follow: describing some silicon defects40, the graphite-diamond transition41, bulk zinc oxide42, copper with some defects”
End-to-End Differentiable , Hardware Accelerated , Molecular Dynamics in Pure Python (2019, background): “To address these issues, several projects developed adapters (Artrith & Urban, 2016; Artrith et al., 2017; Lot et al., 2019; Onat et al., 2018) between common ML languages, like Torch and Tensorflow, and common MD languages like LAMMPS.”