Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input

Abstract: In this work we propose simple, effective and computationally efficient transfer learning approaches for structure-property relation predictions in the context of materials, with highly informative input from different modalities. As materials properties stand from their electronic structure, representations are extracted directly from datasets of electronic charge density profile images using Neural Networks. We demonstrate transferability of the existing pre-trained Convolutional Neural Networks and LLMs knowledge to physics domain data, exploring a wide set of compositions for the regression of energetics- or structure- related properties, and the role of semantic crystallographic information in the context of multimodal approaches. We test the applicability of the CLIP multimodal model, and employ as well a training protocol for building a more interpretable and versatile stacked custom solution from different pre-trained modalities. The study offers a promising avenue for enhancing the effectiveness of descriptor identification in physical systems, shedding light on the power of multimodal transfer learning for materials property prediction. Instead of using the well-established GNN-based approaches, we explore the transfer learning of image- and text-based architectures, which can impact decision making for new low-cost AI methods in the field of Materials and Chemoinformatics.