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 modelling.
@article{arxiv.2202.01449,
title = {Predicting tensorial molecular properties with equivariant machine-learning models},
author = {Vu Ha Anh Nguyen and Alessandro Lunghi},
journal= {arXiv preprint arXiv:2202.01449},
year = {2022}
}