Conference Tools for 2021 TMS Annual Meeting & Exhibition

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Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Accelerating the Discovery of Self-Reporting Redox-active Materials Using Quantum Chemistry Guided Machine Learning
Author(s)	Garvit Agarwal, Hieu A. Doan, Lily A. Robertson, Lu Zhang, Rajeev S. Assary
On-Site Speaker (Planned)	Garvit Agarwal
Abstract Scope	Redox flow batteries (RFBs) are a promising technology for stationary energy storage applications due to their flexible design, easy scalability and low cost. In RFBs, energy is carried in flowable redox-active materials (anolyte and catholyte redoxmers) which are stored externally and pumped to the cell during operation. Further improvement in energy density of RFBs requires design of redoxmers with optimal properties i.e. wider redox potential window, higher solubility, and stability. Additionally, designing redoxmers with fluorescence enabled self-reporting functionality allows monitoring of the state-of-health of RFBs. Here we employ high-throughput DFT calculations to generate database of reduction potentials, solvation free energies and absorption wavelengths of 1000 anolytes. Using simulated data, we develop machine learning models to predict properties from text-based representation (SMILES) of molecular materials. We demonstrate the efficiency of our active learning model, using multi-objective Bayesian optimization, for discovering promising redoxmers with desirable properties from unseen database of 100,000 molecules.
Proceedings Inclusion?	Planned:

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