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Segment Descriptor Enabling Prediction of Electronic Properties and Photocatalytic Hydrogen Evolution Rate of Alternating Conjugated Copolymers Based on Machine Learning

Alternating conjugated copolymers have been regarded
as promising candidates for photocatalytic hydrogen evolution due to the adjustability
of their molecular structures and electronic properties. In this work, machine
learning (ML) models with molecular fingerprint of segment descriptors (SD)
have been successfully constructed to promote the accurate and universal
prediction of electronic properties such as electron affinity, ionization
potential and optical bandgap. Moreover, without any experimental values, a
high-performance prediction classifier model toward photocatalytic hydrogen
production of alternating copolymers has been developed with high accuracy
(real-test accuracy = 0.91). Consequently, our results demonstrate accurate
regression and classification models to disclose valuable influencing factors
concerning hydrogen evolution rate (HER) of alternating copolymers.