Machine-learning models of matter beyond interatomic potentials

Combining electronic structure calculations and machine learning (ML) techniques has become a common approach in the atomistic modeling of matter. Using the two techniques together has allowed researchers, for instance, to create models that use atomic coordinates as the only inputs to inexpensively predict any property that can be computed by the first-principles calculations that had been used to train them.

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