Accelerating amine-based CO₂ capture with machine learning: From molecular screening to process optimization

Amine-based CO₂ capture represents the most mature approach for large-scale carbon reduction, with systems implemented across multiple industrial demonstration projects globally. However, vast chemical spaces encompassing millions of potential formulations and complex multiscale coupling effects pose unprecedented challenges for traditional experimental methods. Machine learning applications have achieved revolutionary advances through differentiated strategies. In liquid amine systems, ensemble learning algorithms delivered breakthrough precision improvements from traditional 4-5% to below 0.93%, while interpretable models revealed that nitrogen atom charge distribution contributes 56% to reaction barriers, enabling rational biphasic solvent design (DETA/DEEA system) that achieved 34% regeneration energy reduction compared to benchmark MEA. For solid amine systems, differential descriptor methods overcame severe overfitting challenges, improving test set performance from R ² = 0.5102 to 0.79. Virtual screening of 1.6 million binding sites from the GDB-17 database identified 11% of candidates with stronger CO₂ binding than the industrial benchmark BPEI (-0.04 eV). Among these high-performance candidates, 2642 molecules simultaneously satisfied synthesizability criteria (SAscore < 3.4, GDBscore > 0.64), demonstrating both favorable binding energetics and high experimental feasibility. Critically, mechanistic analysis revealed that support physical properties dominate adsorption performance over amine chemical characteristics, fundamentally transforming material design concepts. Industrial applications demonstrated 35.76% cost reductions through intelligent solvent selection and 15-25% profit improvements through dynamic capture-level optimization combined with market-responsive bidding strategies. Despite these breakthroughs, systematic limitations, including model generalization difficulties, cross-scale integration challenges, and data standardization, persist, requiring physics-constrained algorithms and unified modeling frameworks for laboratory-to-industrial translation. These developments establish machine learning as the core driving force transitioning amine-based CO₂ capture from empirical development toward intelligent design paradigms.