AUTHOR=Elmegreen Bruce , Hamann Hendrik F. , Wunsch Benjamin , Van Kessel Theodore , Luan Binquan , Elengikal Tonia , Steiner Mathias , Neumann Barros Ferreira Rodrigo , Ohta Ricardo Luis , Oliveira Felipe Lopes , McDonagh James L. , O’Conchuir Breanndan , Zavitsanou Stamatia , Harrison Alexander , Cipcigan Flaviu , de Mel Geeth , La Young-Hye , Sharma Vidushi , Zubarev Dmitry Yu TITLE=MDLab: AI frameworks for carbon capture and battery materials JOURNAL=Frontiers in Environmental Science VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1204690 DOI=10.3389/fenvs.2023.1204690 ISSN=2296-665X ABSTRACT=

There is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. This paper describes novel computational tools for accelerated discovery of solvents, nano-porous materials, and electrolytes. These tools have produced interesting results so far, such as the identification of a relatively isolated location in amine configuration space for the solvents with known carbon capture use, and the demonstration of an end-to-end simulation and process model for carbon capture in MOFs.