Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. (arXiv:2206.06422v1 [cond-mat.mtrl-sci])

Particle-based modeling of materials at atomic scale plays an important role
in the development of new materials and understanding of their properties. The
accuracy of particle simulations is determined by interatomic potentials, which
allow to calculate the potential energy of an atomic system as a function of
atomic coordinates and potentially other properties. First-principles-based ab
initio potentials can reach arbitrary levels of accuracy, however their
aplicability is limited by their high computational cost.

Machine learning (ML) has recently emerged as …

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