Clustering molecular-orbital training data into locally linear groups before regression cuts MOB-ML training time by up to 35,000-fold while retaining chemical accuracy.
Atomic-scale representation and statistical learning of tensorial properties
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule.
fields
physics.chem-ph 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning
Clustering molecular-orbital training data into locally linear groups before regression cuts MOB-ML training time by up to 35,000-fold while retaining chemical accuracy.