Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks


Redox-active organic molecules, i.e., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern in silico techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between ab initio data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules.

SEEK ID: https://publications.h-its.org/publications/1685

Filename: 2023_Fedorov_JCTC.pdf 

Format: PDF document

Size: 12.9 MB

SEEK ID: https://publications.h-its.org/publications/1685

DOI: 10.1021/acs.jctc.3c00355

Research Groups: Computational Carbon Chemistry, SIMPLAIX

Publication type: Journal

Journal: Journal of Chemical Theory and Computation

Citation: J. Chem. Theory Comput. 19(15):4796-4814

Date Published: 8th Aug 2023

Registered Mode: by DOI

Fedorov, R., & Gryn’ova, G. (2023). Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks. In Journal of Chemical Theory and Computation (Vol. 19, Issue 15, pp. 4796–4814). American Chemical Society (ACS). https://doi.org/10.1021/acs.jctc.3c00355

Views: 1334   Downloads: 1

Created: 4th Aug 2023 at 09:53

Last updated: 11th Mar 2024 at 13:33

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