Bayesian Optimization for the Discovery of Redox Active Quinones

Giacomo De Gobbi, Reyhan Yagmur, Janine Maier, Stefan Spirk, Robert Peharz

Keywords: molecular design, Bayesian optimization, redox-flow batteries, AI for Science

TL;DR: Designing a large-scale system for fast screening of redox-active benzoquinones for organic flow batteries

Abstract: Traditional computational chemistry techniques are often a severe bottleneck in scaling up the discovery of materials or new useful molecules. Machine learning techniques have proven effective to overcome such limitations and often lead to unprecedented results. In this work we focus on finding candidate molecules that are benzoquinones derivatives to be used in organic flow batteries. We present a sampling algorithm equipped with chemistry-based constraints in order to generate a molecular library and we utilize a Bayesian optimization strategy to select the best suited molecules.

Submission Number: 247