Grisafi, A and Wilkins, DM and Csanyi, G and Ceriotti, M (2017) *Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.* Phys Rev Lett, 120. 036002-036002. ISSN 0031-9007

## Abstract

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

Item Type: | Article |
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Uncontrolled Keywords: | cond-mat.mtrl-sci cond-mat.mtrl-sci cond-mat.stat-mech |

Subjects: | UNSPECIFIED |

Divisions: | Div C > Applied Mechanics |

Depositing User: | Cron Job |

Date Deposited: | 04 Oct 2017 20:05 |

Last Modified: | 02 Mar 2021 07:34 |

DOI: | 10.1103/PhysRevLett.120.036002 |