Graph-Text Contrastive Learning of Inorganic Crystal Structure toward a Foundation Model of Inorganic Materials

29 March 2024, Version 3
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Developing foundation models for materials science has attracted attention. However, there is a lack of work on inorganic materials due to the difficulty in the comprehensive representation of geometric concepts composing crystals: the local atomic environments, their connections, and the global symmetries. We present a contrastive learning of inorganic crystal structure (CLICS) for embedding the geometric concepts, which contrasts texts representing the contextual patterns of geometries with the crystal graphs. We demonstrate that the geometric concepts are integrally embedded on CLICS feature space, through experiments of concept retrieval from crystal graphs, similar structure search, and few-shot/imbalanced crystal structure classification.

Keywords

Materials informatics
Inorganic materials
Crystal structure
Foundation model
Contrastive learning
Language model
Graph neural network
Machine learning

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