1. MDLab: AI frameworks for carbon capture and battery materials
- Author
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Bruce Elmegreen, Hendrik F. Hamann, Benjamin Wunsch, Theodore Van Kessel, Binquan Luan, Tonia Elengikal, Mathias Steiner, Rodrigo Neumann Barros Ferreira, Ricardo Luis Ohta, Felipe Lopes Oliveira, James L. McDonagh, Breanndan O’Conchuir, Stamatia Zavitsanou, Alexander Harrison, Flaviu Cipcigan, Geeth de Mel, Young-Hye La, Vidushi Sharma, and Dmitry Yu Zubarev
- Subjects
carbon capture ,chemical informatics ,amines ,computational chemistry ,nanoporous materials ,electrolytes ,Environmental sciences ,GE1-350 - 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.
- Published
- 2023
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