Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

DOI

Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120, 036002 (2018). The archive entry contains files in extended-XYZ format including the structures and several tensorial properties: for the monomer, dimer and Zundel cation, the dipole moment, polarizability and first hyperpolarizability are included, and for bulk water the dipole moment, polarizability and dielectric tensor are given.

Identifier
DOI http://dx.doi.org/doi:10.24435/materialscloud:2018.0009/v1
Source https://archive.materialscloud.org/2018.0009/v1
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:2018.0009/v1
Provenance
Creator Grisafi, Andrea;Csányi, Gabor;Ceriotti, Michele;Wilkins, David M.
Publisher Materials Cloud
Publication Year 2018
Rights Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode;info:eu-repo/semantics/openAccess
OpenAccess true
Contact Materials Cloud
Representation
Language English
Resource Type Dataset
Coverage
Discipline Materials Science And Engineering