Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
- 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
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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.
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References
Journal reference A. Grisafi, D. M. Wilkins, G. Csányi, M. Ceriotti, Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems, Phys. Rev. Lett. 120, 036002 (2018), doi: 10.1103/PhysRevLett.120.036002