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:
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.


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.

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File name Size Description
1.3 KiB Description of the content and format of the other files.
583.0 KiB Structures and tensors for water monomers.
674.5 KiB Structures and tensors for water dimers.
701.1 KiB Structures and tensors for Zundel cation.
3.3 MiB Structures and tensors for bulk water.


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


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