AI-based Catalytic Performance Prediction for CO2 Electrochemical Reduction Using Ionic Liquids
Wednesday, June 24, 2026 3:45 PM to 5:15 PM · 1 hr. 30 min. (Europe/Berlin)
Foyer D-G - 2nd Floor
Women in HPC Poster
AI Applications powered by HPC TechnologiesChemistry and Materials ScienceHigh-Performance Data AnalyticsHPC Simulations enhanced by Machine LearningRenewable Energy
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Poster is on display and will be presented at the poster pitch session.
Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO2 electrochemical reduction (CO2ER) due to their unique properties viz. thermal and chemical stability, good CO2 solubility, and their potential to reduce overpotential. The catalytic performance of ILs in CO2ER has been explored via experimental methods which have limitations, given the unclear understanding of the complex reaction mechanisms. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. In this study, we investigate two categories of AI models (ML and DL) for predicting the CO2ER catalytic performance via Gibbs free energy and capacity. For this task, we formulate a novel dataset (CO2ERIL) from 90 ILs in two formats using: (1) 30 electronic and geometric IL properties, (2) IL chemical structures in SMILES data format. The dataset versions are formulated using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE software. The prediction results of the AI models on the two dataset versions depict a similar trend in the predicted target variables signifying that the dataset format does not really affect the model performance. Moreover, the best ML model outperforms the DL model for one target variable, Free energy predictions, but lags in the other target variable, capacity predictions. Our study is novel in terms of AI methodology as well as the CO2ER dataset. Our research outcomes will contribute to the more effective selection of ILs for CO2ER catalysis.
Contributors:
Recently, ionic liquids (ILs) have garnered remarkable attention as electrolytes for CO2 electrochemical reduction (CO2ER) due to their unique properties viz. thermal and chemical stability, good CO2 solubility, and their potential to reduce overpotential. The catalytic performance of ILs in CO2ER has been explored via experimental methods which have limitations, given the unclear understanding of the complex reaction mechanisms. Recently, Artificial Intelligence (AI) methods have gained increased attention across diverse applications including chemical engineering. These methods play a pivotal role in extracting insights, understanding patterns, and mitigating uncertainty within datasets. In this study, we investigate two categories of AI models (ML and DL) for predicting the CO2ER catalytic performance via Gibbs free energy and capacity. For this task, we formulate a novel dataset (CO2ERIL) from 90 ILs in two formats using: (1) 30 electronic and geometric IL properties, (2) IL chemical structures in SMILES data format. The dataset versions are formulated using the Conductor-like Screening Model for Realistic Solvents (COSMO-RS) and TURBOMOLE software. The prediction results of the AI models on the two dataset versions depict a similar trend in the predicted target variables signifying that the dataset format does not really affect the model performance. Moreover, the best ML model outperforms the DL model for one target variable, Free energy predictions, but lags in the other target variable, capacity predictions. Our study is novel in terms of AI methodology as well as the CO2ER dataset. Our research outcomes will contribute to the more effective selection of ILs for CO2ER catalysis.
Contributors:
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