Predicting redox potentials by graph‐based machine learning methods
Linlin Jia,
Éric Brémond,
Larissa Zaida
et al.
Abstract:The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical computations, which is a complementary task and sometimes the only alternative to experimental measurement, may be often resource‐intensive and time‐consuming. This paper addresses this challenge through the application of machine learning techniques, with a particular focus on graph‐based methods (such as graph edit distances, graph kernels, and graph neural netw… Show more
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