A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

Abstract

Effective carbon dioxide (CO2) capture plays indispensable roles in closing the global carbon cycle, serving the sustainable production of energy, and achieving the grand 1.5 °C goal by 2050. Considering the diversity and complexity of CO2 capture materials, machine learning has stepped into this field years ago and become a powerful tool that promotes the screening and design of involving parameters. From these perspectives, this critical review firstly summarizes the technical backgrounds for the applications of ML-based methods in CO2 capture. Then, through categorizing the materials into two major groups, that is, adsorbents (containing metal organic frameworks, carbonaceous materials, polymers, and zeolites) and absorbents (involving ionic liquids, amine-based absorbents, and deep eutectic solvents), the applications of this effective tool in relevant areas are scrutinized. The major concerns remain to be further addressed are derived based on the above discussions, namely 1) the development of consistent and integrated databases, 2) the wise digitalization of inherent properties of materials, and 3) the validation of the accuracy of ML-derived results under practical scenarios. The main purpose of this critical review is bridging the previous achievements and further developments of ML-assisted design of CO2 capture techniques.