Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents
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 model...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000096 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585042092621824 |
|---|---|
| author | Shuangjun Li Zhixin Huang Yuanming Li Shuai Deng Xiangkun Elvis Cao |
| author_facet | Shuangjun Li Zhixin Huang Yuanming Li Shuai Deng Xiangkun Elvis Cao |
| author_sort | Shuangjun Li |
| collection | DOAJ |
| description | 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 CO2 adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO2 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. |
| format | Article |
| id | doaj-art-035249dc6eea431ba316fc6ed3bd3efe |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-035249dc6eea431ba316fc6ed3bd3efe2025-01-27T04:22:24ZengElsevierEnergy and AI2666-54682025-05-0120100477Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbentsShuangjun Li0Zhixin Huang1Yuanming Li2Shuai Deng3Xiangkun Elvis Cao4Department of Chemical and Biological Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South KoreaState Key Laboratory of Engines, Tianjin University, Tianjin 300350, China; International Cooperation Research Centre of Carbon Capture in Ultra-low Energy-consumption, Tianjin 300350, ChinaSchool of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea; Corresponding author.State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China; International Cooperation Research Centre of Carbon Capture in Ultra-low Energy-consumption, Tianjin 300350, China; Corresponding author at: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China.Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge 02139, USA; Corresponding author.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 CO2 adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO2 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.http://www.sciencedirect.com/science/article/pii/S2666546825000096Materials informaticsMachine learningLarge language modelsContext-based modelingSolid aminesDirect air capture |
| spellingShingle | Shuangjun Li Zhixin Huang Yuanming Li Shuai Deng Xiangkun Elvis Cao Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents Energy and AI Materials informatics Machine learning Large language models Context-based modeling Solid amines Direct air capture |
| title | Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents |
| title_full | Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents |
| title_fullStr | Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents |
| title_full_unstemmed | Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents |
| title_short | Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents |
| title_sort | methodology for predicting material performance by context based modeling a case study on solid amine co2 adsorbents |
| topic | Materials informatics Machine learning Large language models Context-based modeling Solid amines Direct air capture |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000096 |
| work_keys_str_mv | AT shuangjunli methodologyforpredictingmaterialperformancebycontextbasedmodelingacasestudyonsolidamineco2adsorbents AT zhixinhuang methodologyforpredictingmaterialperformancebycontextbasedmodelingacasestudyonsolidamineco2adsorbents AT yuanmingli methodologyforpredictingmaterialperformancebycontextbasedmodelingacasestudyonsolidamineco2adsorbents AT shuaideng methodologyforpredictingmaterialperformancebycontextbasedmodelingacasestudyonsolidamineco2adsorbents AT xiangkunelviscao methodologyforpredictingmaterialperformancebycontextbasedmodelingacasestudyonsolidamineco2adsorbents |