Kichev, I.; Borislavov, L.; Tadjer, A.; Stoyanova, R. Machine Learning Prediction of the Redox Activity of Quinones. Materials2023, 16, 6687.
Kichev, I.; Borislavov, L.; Tadjer, A.; Stoyanova, R. Machine Learning Prediction of the Redox Activity of Quinones. Materials 2023, 16, 6687.
Kichev, I.; Borislavov, L.; Tadjer, A.; Stoyanova, R. Machine Learning Prediction of the Redox Activity of Quinones. Materials2023, 16, 6687.
Kichev, I.; Borislavov, L.; Tadjer, A.; Stoyanova, R. Machine Learning Prediction of the Redox Activity of Quinones. Materials 2023, 16, 6687.
Abstract
The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, the redox properties could be precisely shaped by using different substituent groups. Machine learning and statistics, onthe other hand, have proven to be very powerful approaches for efficient in silico design of novel materials. Herein, we demonstrated the machine learning approach for the prediction of the redox activity of quinones that potentially can serve as organic battery components. For the needs of the present study, a database of small quinone-derived molecules was created. A large number of quantum chemical and chemometrics descriptors was generated for each molecule and subsequently different statistical approaches were applied to select the descriptors that most prominently characterize the relationship between structure and redox-potential. Various machine-learning methods for screening of prospective organic battery electrode materials were deployed to select the most trustworthy strategy for machine learning aided design of organic redox materials. It was found that ridge regression models perform better than regression decision tree and decision tree based ensemble algorithms.
Chemistry and Materials Science, Theoretical Chemistry
Copyright:
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