materialscloud:2018.0009/v1

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

Andrea Grisafi1, David M. Wilkins1*, Gabor Csányi2, Michele Ceriotti1

1 Laboratory of Computational Science and Modeling, IMX, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2 Engineering Laboratory, University of Cambridge, Trumpington Street, Cambridge CB21PZ, United Kingdom

* Corresponding authors emails: david.wilkins@epfl.ch
DOI10.24435/materialscloud:2018.0009/v1 [version v1]

Publication date: May 19, 2018

How to cite this record

Andrea Grisafi, David M. Wilkins, Gabor Csányi, Michele Ceriotti, Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems, Materials Cloud Archive 2018.0009/v1 (2018), doi: 10.24435/materialscloud:2018.0009/v1.

Description

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.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.

Files

File name Size Description
README.txt
MD5md5:2b977c3fc6b23f7ff894777c7bacc9cc
1.3 KiB Description of the content and format of the other files.
water_monomer.xyz
MD5md5:efa6e057541497f86eeffa603cdaa539
583.0 KiB Structures and tensors for water monomers.
water_dimer.xyz
MD5md5:830dc887aa0562669d4eea80db4365f6
674.5 KiB Structures and tensors for water dimers.
zundel.xyz
MD5md5:1cee4b970d00be9893f06cbc0bda4adf
701.1 KiB Structures and tensors for Zundel cation.
water_bulk.xyz
MD5md5:7f79f3dbc9758210c8f2402e6742663a
3.3 MiB Structures and tensors for bulk water.

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

Keywords

water molecular bulk dipole moment polarizability hyperpolarizability dielectric tensor symmetry-adapted gaussian process regression machine learning

Version history:

2018.0009/v1 (version v1) [This version] May 19, 2018 DOI10.24435/materialscloud:2018.0009/v1