Multimodal machine learning for materials science: composition-structure bimodal learning for experimentally measured properties

TL;DR

This paper presents COSNet, a bimodal machine learning model that combines composition and structure data to improve predictions of materials properties, especially when structural data is incomplete, outperforming composition-only models.

Contribution

The paper introduces a novel bimodal learning approach for materials science that effectively integrates composition and structure data, enhancing property prediction accuracy.

Findings

Bimodal learning reduces prediction errors across various properties.

COSNet outperforms composition-only learning methods.

Data augmentation based on modal availability is crucial for success.

Abstract

The widespread application of multimodal machine learning models like GPT-4 has revolutionized various research fields including computer vision and natural language processing. However, its implementation in materials informatics remains underexplored, despite the presence of materials data across diverse modalities, such as composition and structure. The effectiveness of machine learning models trained on large calculated datasets depends on the accuracy of calculations, while experimental datasets often have limited data availability and incomplete information. This paper introduces a novel approach to multimodal machine learning in materials science via composition-structure bimodal learning. The proposed COmposition-Structure Bimodal Network (COSNet) is designed to enhance learning and predictions of experimentally measured materials properties that have incomplete structure information. Bimodal learning significantly reduces prediction errors across distinct materials properties including Li conductivity in solid electrolyte, band gap, refractive index, dielectric constant, energy, and magnetic moment, surpassing composition-only learning methods. Furthermore, we identified that data augmentation based on modal availability plays a pivotal role in the success of bimodal learning.