Predicting tensorial molecular properties with equivariant machine learning models

Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks. These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modeling.

Automated summary

Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties. In this work, the authors propose a scalable equivariant machine learning model based on local atomic environment descriptors for accurate predictions of dielectric and magnetic tensorial properties of different ranks in molecules.