Gaussian Process Regression for Materials and Molecules

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field.

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• 1 The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities.
• 2 Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed.
• 3 A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field.

This work deepens our understanding of the fundamental laws governing the universe, from subatomic particles to cosmic structures.

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