LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential

James Barker (), Johannes Bulin, Jan Hamaekers and Sonja Mathias
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James Barker: Schloss Birlinghoven, Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Johannes Bulin: Schloss Birlinghoven, Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Jan Hamaekers: Schloss Birlinghoven, Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Sonja Mathias: Schloss Birlinghoven, Fraunhofer Institute for Algorithms and Scientific Computing SCAI

A chapter in Scientific Computing and Algorithms in Industrial Simulations, 2017, pp 25-42 from Springer

Abstract: 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 in both prediction accuracy and computational cost when compared to similar Coulomb matrix-based methods.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-62458-7_2

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DOI: 10.1007/978-3-319-62458-7_2

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