Chemical discovery with Bayesian opitimization for higher efficacy and accuracy
JACS Au, ASAP (2022)
J. Am. Chem. Soc., 143, 42, 17535–17547 (2021)
J. Phys. Chem. Lett., 11, 19, 8067–8076 (2020)
ACS Cent. Sci., 6, 413–524 (2020)
With improved autonomous workflow for computational high throughput screening enabled by machine learning "decision engines" that I built, we can obtain quantum chemistry data sets with much more ease and higher fidelity,
This autonomous workflow can then be coupled with established materials discovery strategies, such as active learning, Bayesian optimization, and uncertainty quantification. This way, we discovered promising materials candidates as redox couples in redox flow batteries, catalysts for methane-to-methanol conversion, and transition metal chromophores.