High-throughput computational screening and thermodynamics-informed machine learning for lithium-based ceramic materials for high-temperature CO2 capture.

First-principles thermodynamic screening identified 120 reversible carbonation reactions of lithium-based ceramic sorbents for high-temperature CO2 capture. A reaction-level graph neural network with transfer learning was proposed that efficiently predicted the temperature dependence of the carbonation reaction.