Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction

• URL: http://arxiv.org/abs/2308.16259v2
• Date: Fri, 1 Sep 2023 12:40:29 GMT
• Title: Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction
• Authors: Hongshuo Huang, Rishikesh Magar, Changwen Xu and Amir Barati Farimani
• Abstract summary: We introduce our model Materials Informatics Transformer (MatInFormer) for material property prediction. Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information.
• Score: 6.349503549199403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the remarkable capabilities of large language models (LLMs) have been illustrated across a variety of research domains such as natural language processing, computer vision, and molecular modeling. We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer). Specifically, we introduce a novel approach that involves learning the grammar of crystallography through the tokenization of pertinent space group information. We further illustrate the adaptability of MatInFormer by incorporating task-specific data pertaining to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover the key features that the model prioritizes during property prediction. The effectiveness of our proposed model is empirically validated across 14 distinct datasets, hereby underscoring its potential for high throughput screening through accurate material property prediction.

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