Localized Coulomb Descriptors for the Gaussian Approximation Potential

2016/11/16 by James Barker, Johannes Bulin, Barker, James +5 · 1 citation
Materials Science · Engineering · #Machine Learning in Materials Science #Reservoir Engineering and Simulation Methods #Fault Detection and Control Systems

paper · pdf · doi:10.48550/arxiv.1611.05126

Abstract

We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential approach, we present LC-GAP, a new system for generating atomic potentials through machine learning (ML). Tests on the QM7, QM7b and GDB9 biomolecular datasets demonstrate that potentials created with LC-GAP can successfully predict atomization energies for molecules larger than those used for training to chemical accuracy, and can (in the case of QM7b) also be used to predict a range of other atomic properties with accuracy in line with the recent literature. As the best-performing representation has only linear dimensionality in the number of atoms in a local atomic environment, this represents an improvement both in prediction accuracy and computational cost when considered against similar Coulomb matrix-based methods.

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