Localized Coulomb descriptors for the Gaussian Approximation Potential
Localized Coulomb descriptors for the Gaussian Approximation Potential
dc.contributor.author | Barker, James | |
dc.contributor.author | Bulin, Johannes | |
dc.contributor.author | Hamaekers, Jan | |
dc.contributor.author | Mathias, Sonja | |
dc.date.accessioned | 2024-08-15T11:06:02Z | |
dc.date.available | 2024-08-15T11:06:02Z | |
dc.date.issued | 02.2016 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11811/11848 | |
dc.description.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. | en |
dc.format.extent | 19 | |
dc.language.iso | eng | |
dc.relation.ispartofseries | INS Preprints ; 1604 | |
dc.rights | In Copyright | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject.ddc | 510 Mathematik | |
dc.subject.ddc | 518 Numerische Analysis | |
dc.title | Localized Coulomb descriptors for the Gaussian Approximation Potential | |
dc.type | Preprint | |
dc.publisher.name | Institut für Numerische Simulation (INS) | |
dc.publisher.location | Bonn | |
dc.rights.accessRights | openAccess | |
dc.relation.doi | https://doi.org/10.48550/arXiv.1611.05126 | |
ulbbn.pubtype | Zweitveröffentlichung | |
dcterms.bibliographicCitation.url | https://ins.uni-bonn.de/publication/preprints |
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