Work

Scalar machine learning of tensorial quantities—Born effective charges from monopole models

Year: 2026

Type: article

Abstract: Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for learning tensorial quantities based on scalar descriptors. We apply this approach to the Born effective charge tensor, showing that scalar (monopole) kernel models can successfully capture its tensorial nature by exploiting the definition of the Born effective charge tensor as the derivative of the polarization with respect to atomic displacements. We compare this method with tensorial (dipole) kernel models, as established in our previous work, in which the tensorial structure of the Born effective charge is encoded directly in the kernel and obtained via its derivative. Both approaches are then used for charge partitioning, enabling the separation of monopole and dipole contributions. Finally, we demonstrate the effectiveness of the framework by computing finite-temperature infrared spectra for a range of complex materials.

Source: The Journal of Chemical Physics

Authors: Bernhard Schmiedmayer, Angela Rittsteuer, Tobias Hilpert, Georg Kresse

Institutions: University of Vienna, VASP Software (Austria)

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,500

Funder: Austrian Science Fund

Awards: G163165086, G9000084739