AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture
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arXiv
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| Format: | Preprint |
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2026
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| author | Garbelotto, Davide Lobo, Alexander Awasthi, Urvi Medvedev, Oleg Mukherjee, Srayanta Aristov, Anton Polunin, Konstantin De Mur, Alex Zhukov, Leonid Huseynov, Azad Abdullayev, Murad |
| author_facet | Garbelotto, Davide Lobo, Alexander Awasthi, Urvi Medvedev, Oleg Mukherjee, Srayanta Aristov, Anton Polunin, Konstantin De Mur, Alex Zhukov, Leonid Huseynov, Azad Abdullayev, Murad |
| contents | We present an AI-driven approach to discover compounds with optimal properties for CO2 capture from flue gas-refinery emissions' primary source. Focusing on ionic liquids (ILs) as alternatives to traditional amine-based solvents, we successfully identify new IL candidates with high working capacity, manageable viscosity, favorable regeneration energy, and viable synthetic routes. Our approach follows a five-stage pipeline. First, we generate IL candidates by pairing available cation and anion molecules, then predict temperature- and pressure-dependent CO2 solubility and viscosity using a GNN-based molecular property prediction model. Next, we convert solubility to working capacity and regeneration energy via Van't Hoff modeling, and then find the best set of candidates using Pareto optimization, before finally filtering those based on feasible synthesis routes. We identify 36 feasible candidates that could enable 5-10% OPEX savings and up to 10% CAPEX reductions through lower regeneration energy requirements and reduced corrosivity-offering a novel carbon-capture strategy for refineries moving forward. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03284 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture Garbelotto, Davide Lobo, Alexander Awasthi, Urvi Medvedev, Oleg Mukherjee, Srayanta Aristov, Anton Polunin, Konstantin De Mur, Alex Zhukov, Leonid Huseynov, Azad Abdullayev, Murad Chemical Physics Materials Science Artificial Intelligence We present an AI-driven approach to discover compounds with optimal properties for CO2 capture from flue gas-refinery emissions' primary source. Focusing on ionic liquids (ILs) as alternatives to traditional amine-based solvents, we successfully identify new IL candidates with high working capacity, manageable viscosity, favorable regeneration energy, and viable synthetic routes. Our approach follows a five-stage pipeline. First, we generate IL candidates by pairing available cation and anion molecules, then predict temperature- and pressure-dependent CO2 solubility and viscosity using a GNN-based molecular property prediction model. Next, we convert solubility to working capacity and regeneration energy via Van't Hoff modeling, and then find the best set of candidates using Pareto optimization, before finally filtering those based on feasible synthesis routes. We identify 36 feasible candidates that could enable 5-10% OPEX savings and up to 10% CAPEX reductions through lower regeneration energy requirements and reduced corrosivity-offering a novel carbon-capture strategy for refineries moving forward. |
| title | AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture |
| topic | Chemical Physics Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2601.03284 |