2026-04-20T07:55:40Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:43 2018-05-19T00:00:00Z openaire_data community-mcarchive

Wilkins, David M. Grisafi, Andrea Wilkins, David M. Csányi, Gabor Ceriotti, Michele 2018-05-19 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. text/plain chemical/x-xyz chemical/x-xyz chemical/x-xyz chemical/x-xyz text/markdown https://doi.org/10.24435/materialscloud:2018.0009/v1 oai:materialscloud.org:43 mcid:2018.0009/v1 eng Materials Cloud https://doi.org/10.1103/PhysRevLett.120.036002 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:nv-dg info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode water molecular bulk dipole moment polarizability hyperpolarizability dielectric tensor symmetry-adapted gaussian process regression machine learning Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems info:eu-repo/semantics/other