Data-driven estimation of transfer integrals in undoped cuprates

For magnetic insulators such as undoped cuprates, band-structure calculations

based on density-functional theory (DFT) are a reliable source of information

on the microscopic magnetic model. Yet, estimation of the transfer integrals

remains cumbersome, as it requires projections on a carefully crafted Wannier

basis. We demonstrate how an artificial neural network (ANN), trained on

results of high-throughput DFT calculations, can be

employed for the estimation of transfer integrals based exclusively on the

structural information. In particular, the ANN maps the local crystal

environment of two copper sites onto the real value of the respective transfer

integral.  The crystal environment representation is based on the three-dimensional Zernike functions,

which is a truncated expansion in orthogonal multinomials of site positions

functions [1]. It encrypts the spatial configuration of atoms and the chemical

composition, expressed by the oxidation number and the ionicity radius. Our

approach can be employed for a rapid assessment of the spin models of new

cuprate materials.

[1] M. Novotni and R. Klein, Comput. Aided Des. 36, 1047 (2004).