Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents
Shuangjun Li, Zhixin Huang, Yuanming Li, Shuai Deng, Xiangkun Elvis Cao
Abstract
• A novel methodology for predicting material performance by using large language models. • Both numerical and textual information can be treated as input of the model. • Solid amine CO 2 adsorbents in the DAC application were studied as a case study. Traditional materials informatics leverages big data and machine learning (ML) to forecast material performance based on structural features but often overlooks valuable textual information. In this work, we proposed a novel methodology for predicting material performance through context-based modeling using large language models (LLMs). This method integrates both numerical and textual information, enhancing predictive accuracy and scalability. In the case study, the approach is applied to predict the performance of solid amine CO 2 adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO 2 adsorption uptake based on input features, including material properties and experimental conditions. The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task. We adopted Sapley Additive exPlanations (SHAP) to further elucidate the importance of various input features. This work highlights the potential of LLMs in materials science, offering a cost-effective, efficient solution for complex predictive tasks.