Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries.

ABSTRACT: Proton-electron transfer (PET) reactions are rather common in chemistry and crucial in energy storage applications. How electrons and protons are involved or which mechanism dominates is strongly molecule and pH dependent. Quantum chemical methods can be used to assess redox potential (E_red.) and acidity constant (pK_a) values but the computations are rather time consuming. In this work, supervised machine learning (ML) models are used to predict PET reactions and analyze molecular space. The data for ML have been created by density functional theory (DFT) calculations. Random forest regression models are trained and tested on a dataset that we created. The dataset contains more than 8200 quinone-type organic molecules that each underwent two proton and t

SUBMITTER: Hashemi A 

PROVIDER: S-EPMC10561546 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature