Machine Guided Discovery of Novel Carbon Capture Solvents

Abstract

This study relates to the experimental characterization of amine solutions and the use of machine learning methods to classify and predict novel amines for carbon dioxide capture. Carbon Capture and Storage (CCUS) is a critical element of world efforts to mitigate the effects of climate change. Amine solvents have been successfully applied to large-scale CCUS implementations, but material challenges include the cost of regeneration energy, solution degradation, and corrosivity. Improvements in stability, binding capacity, kinetics, and vapor-liquid equilibrium have been achieved through improve chemistry,. but accelerating their discovery with machine learning and AI has seen limited exploration. Machine learning provides a promising method for reducing the time and resource burdens of materials development through efficient correlation of structure-property relationships to allow down-selection and focusing on promising candidates. Towards demonstrating this, we have developed an end-to-end "discovery cycle" to select new aqueous amines compatible with the commercially viable acid gas scrubbing for carbon capture. We combine a simple, rapid laboratory assay for CO2 absorption with a machine learning based molecular fingerprinting model approach. The prediction process shows 60% accuracy against experiment for both material parameters and 80% for a single parameter on an external test set. The discovery cycle determined several promising amines that were verified experimentally, and which had not been applied to carbon capture previously. In the process we have compiled a large, single-source data set for carbon capture amines and produced an open-source machine learning tool for the identification of amine molecule candidates.