In this section
@article{Li:2025:10.1016/j.egyai.2025.100477,
author = {Li, S and Huang, Z and Li, Y and Deng, S and Cao, XE},
doi = {10.1016/j.egyai.2025.100477},
journal = {Energy and AI},
title = {Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents},
url = {http://dx.doi.org/10.1016/j.egyai.2025.100477},
volume = {20},
year = {2025}
}
TY - JOUR
AB - 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<inf>2</inf> adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO<inf>2</inf> 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.
AU - Li,S
AU - Huang,Z
AU - Li,Y
AU - Deng,S
AU - Cao,XE
DO - 10.1016/j.egyai.2025.100477
PY - 2025///
TI - Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents
T2 - Energy and AI
UR - http://dx.doi.org/10.1016/j.egyai.2025.100477
VL - 20
ER -
Climate Science
Earth and Life Sciences
Energy and Low-Carbon Futures
Resources and Pollution
Economics and Finance
Health
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