Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2023-10-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
1565-1576
Series
Digital Discovery, Volume 2, issue 5
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 (Ered.) and acidity constant (pKa) 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 two electron transfer reactions. Both structural and chemical descriptors are used. The HOMO of the reactant and LUMO of the product participating in the oxidation reaction appeared to be strongly associated with Ered.. Trained models using a SMILES-based structural descriptor can efficiently predict the pKa and Ered. with a mean absolute error of less than 1 and 66 mV, respectively. Good prediction accuracy of R2 > 0.76 and >0.90 was also obtained on the external test set for Ered. and pKa, respectively. This hybrid DFT-ML study can be applied to speed up the screening of quinone-type molecules for energy storage and other applications.Description
Funding Information: We acknowledge the Digipower project, supported by Teknologiateollisuuden 100v säätiö and Jane ja Aatos Erkon säätiö, and the Horizon 2020 Framework Programme CompBat with Project No. 875565, for financing this study. M. B. acknowledges financial support through the Dr Barbara Mez-Starck Foundation. This work has also partially emanated from the research of P. P. supported by the European Research Council (Starting Grant, agreement no. 950038). P. P. also gratefully acknowledges the Academy Research Fellow funding by the Academy of Finland (Grant No. 315739). We also thank CSC-IT Center for Science Ltd for generous grants of computer time. Publisher Copyright: © 2023 RSC.
Keywords
Other note
Citation
Hashemi , A , Khakpour , R , Mahdian , A , Busch , M , Peljo , P & Laasonen , K 2023 , ' Density functional theory and machine learning for electrochemical square-scheme prediction: an application to quinone-type molecules relevant to redox flow batteries ' , Digital Discovery , vol. 2 , no. 5 , pp. 1565-1576 . https://doi.org/10.1039/d3dd00091e
