AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture

2026-01-02

AI Summary

This study addresses CO₂ capture from industrial flue gas by proposing an AI-driven, end-to-end design framework for ionic liquid solvents as sustainable alternatives to energy-intensive and corrosive amine-based systems. The approach generates candidate molecules through combinatorial pairing of cations and anions, employs graph neural networks to predict CO₂ solubility and viscosity, and utilizes the Van’t Hoff model to estimate working capacity and regeneration energy. Integrated Pareto-based multi-objective optimization and synthetic feasibility analysis enable a closed-loop pipeline encompassing molecular generation, property prediction, performance optimization, and synthesizability validation. The method successfully identifies 36 high-performance ionic liquid candidates, projected to reduce operational costs by 5–10% and capital expenditures by up to 10%.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Machine Learning: Efficient ML / Green AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• CO2 capture
• ionic liquids
• solvent discovery
• flue gas
• carbon capture

Innovation

• AI-driven discovery
• ionic liquids
• graph neural network (GNN)
• Pareto optimization
• CO2 capture