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This study presents a viable approach for designing eco-friendly ionic liquids (ILs) with enhanced CO2 solubility using language models, specifically GPT-2 in conjunction with SMILES-X. The GPT-2 model was fine-tuned on a relatively small, unlabeled IL dataset and subsequently used to generate diverse IL structures. SMILES-X models, trained on IL datasets labeled with CO2 solubility and eco-toxicity values, were employed to predict the properties of the generated ILs. Trends observed in the predicted IL properties were validated using density functional theory (DFT) and COSMO-RS calculations. The GPT-2 model was then fine-tuned iteratively, with the training data updated by including the top generated ILs from previous cycles. This iterative process led to a gradual improvement in the properties of the generated ILs. It was also observed, however, that continuously adding curated generated ILs to the training data eventually caused the model to produce correct but unrealistic IL structures. These findings highlight both the potential and limitations of language models in designing novel chemicals. Additionally, the CO2 adsorption capacity of a surrogate IL was experimentally measured, demonstrating the potential of this approach in advancing decarbonization technologies.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Generative model, GPT, Prediction, SMILES, Decarbonization

刊行年月日: 2025-05-22

出版者: Elsevier BV

掲載誌:

• Artificial Intelligence Chemistry (ISSN: 29497477) vol. 3 issue. 1 100089

研究助成金:

• International Institute for Carbon-Neutral Energy Research, Kyushu University WPI-I2CNER
• Ministry of Education, Culture, Sports, Science and Technology 251FP-22667

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1016/j.aichem.2025.100089

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更新時刻: 2025-12-10 08:30:26 +0900

MDRでの公開時刻: 2025-12-10 08:23:46 +0900