Active Learning Guided Computational Discovery of Plant-Based Redoxmers for Organic Nonaqueous Redox Flow Batteries.

Autor:	Jain A; Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, Illinois 60439, United States.; Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States., Shkrob IA; Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, Illinois 60439, United States.; Chemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United States., Doan HA; Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, Illinois 60439, United States.; Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States., Adams K; Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States., Moore JS; Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.; Beckman Institute for Advanced Science and Technology and Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States., Assary RS; Joint Center for Energy Storage Research (JCESR), Argonne National Laboratory, Lemont, Illinois 60439, United States.; Materials Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States.
Jazyk:	angličtina
Zdroj:	ACS applied materials & interfaces [ACS Appl Mater Interfaces] 2023 Dec 20; Vol. 15 (50), pp. 58309-58319. Date of Electronic Publication: 2023 Dec 10.
DOI:	10.1021/acsami.3c11741
Abstrakt:	Organic nonaqueous redox flow batteries (O-NRFBs) are promising energy storage devices due to their scalability and reliance on sourceable materials. However, finding suitable redox-active organic molecules (redoxmers) for these batteries remains a challenge. Using plant-based compounds as precursors for these redoxmers can decrease their costs and environmental toxicity. In this computational study, flavonoid molecules have been examined as potential redoxmers for O-NRFBs. Flavone and isoflavone derivatives were selected as catholyte (positive charge carrier) and anolyte (negative charge carrier) molecules, respectively. To drive their redox potentials to the opposite extremes, in silico derivatization was performed using a novel algorithm to generate a library of > 40000 candidate molecules that penalizes overly complex structures. A multiobjective Bayesian optimization based active learning algorithm was then used to identify best redoxmer candidates in these search spaces. Our study provides methodologies for molecular design and optimization of natural scaffolds and highlights the need of incorporating expert chemistry awareness of the natural products and the basic rules of synthetic chemistry in machine learning.
Databáze:	MEDLINE
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