  The increasing importance of carbon capture technologies for deployment in
remediating CO2 emissions, and thus the necessity to improve capture materials
to allow scalability and efficiency, faces the challenge of materials
development, which can require substantial costs and time. Machine learning
offers 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 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
(https://github.com/IBM/Carbon-capture-fingerprint-generation).
