Small-data physics-informed ML model for magnetic exchange based on chemical composition and structure

Abstract

In this paper we propose an ensemble model based on a swarm of dense neural networks, able to predict Heisenberg exchange interactions within less than 1% error. The model uses the inputs of the ab-initio Schrödinger equation, i.e. the compound structure and chemical composition. In this first work, we trained the model on a few compounds of the hexagonal manganite family and were able to infer, with similar accuracy, the J values for other compounds of the same family at randomly chosen geometries, thus showing that we accurately learned the effect of the chemical composition and structure on effective magnetic integrals J.