Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining

TL;DR

This paper introduces a novel information fusion framework combining pre-trained language models and contrastive learning to enhance materials property prediction by integrating literature-based knowledge with physical descriptors.

Contribution

It presents an innovative architecture that fuses textual and quantitative data for materials knowledge mining, outperforming traditional methods in complex property prediction.

Findings

Achieved R2 of 0.849 on titanium alloys

Achieved R2 of 0.680 on refractory alloys

Demonstrated superior performance over baseline models

Abstract

Machine learning has revolutionized materials design, yet predicting complex properties like alloy ductility remains challenging due to the influence of processing conditions and microstructural features that resist quantification through traditional reductionist approaches. Here, we present an innovative information fusion architecture that integrates domain-specific texts from materials science literature with quantitative physical descriptors to overcome these limitations. Our framework employs MatSciBERT for advanced textual comprehension and incorporates contrastive learning to automatically extract implicit knowledge regarding processing parameters and microstructural characteristics. Through rigorous ablation studies and comparative experiments, the model demonstrates superior performance, achieving coefficient of determination (R2) values of 0.849 and 0.680 on titanium alloy validation set and refractory multi-principal-element alloy test set. This systematic approach provides a holistic framework for property prediction in complex material systems where quantitative descriptors are incomplete and establishes a foundation for knowledge-guided materials design and informatics-driven materials discovery.