Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

  • Andrea Grisafi (Creator)
  • David Wilkins (Creator)
  • Gábor Csányi (Creator)
  • Michele Ceriotti (Creator)



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.
Date made availableMay 2018
PublisherMaterials Cloud
Date of data production2018 -

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