A Computational Protocol Combining DFT and Cheminformatics for Prediction of pH-Dependent Redox Potentials

Charging the Future: Harnessing Nature's Designs for Bioinspired Molecular Electrodes

The transition toward electric-powered devices is anticipated to play a pivotal role in advancing the global net-zero carbon emission agenda aimed at mitigating greenhouse effects. This shift necessitates a parallel focus on the development of energy storage materials capable of supporting intermittent renewable energy sources. While lithium-ion batteries, featuring inorganic electrode materials, exhibit desirable electrochemical characteristics for energy storage and transport, concerns about the toxicity and ethical implications associated with mining transition metals in their electrodes have prompted a search for environmentally safe alternatives. Organic electrodes have emerged as promising and sustainable alternatives for batteries. This review paper will delve into the recent advancements in nature-inspired electrode design aimed at addressing critical challenges such as capacity degradation due to dissolution, low operating voltages, and the intricate molecular-level processes governing macroscopic electrochemical properties.

High throughput selection of organic cathode materials

Efficient and affordable batteries require the design of novel organic electrode materials to overcome the drawbacks of the traditionally used inorganic materials, and the computational screening of potential candidates is a very efficient way to identify prospective solutions and minimize experimental testing. Here we present a DFT high-throughput computational screening where 86 million molecules contained in the PUBCHEM database have been analyzed and classified according to their estimated electrochemical features. The 5445 top-performing candidates were identified, and among them, 2306 are expected to have a one-electron reduction potential higher than 4 V versus (Li/Li+ ). Analogously, one-electron energy densities higher than 800 Whkg-1 have been predicted for 626 molecules. Explicit calculations performed for certain materials show that at least 69 candidates with a two-electron energy density higher than 1300 Whkg-1 . Successful molecules were sorted into several families, some of them already commonly used electrode materials, and others still experimentally untested. Most of them are small systems containing conjugated CO, NN, or NC functional groups. Our selected molecules form a valuable starting point for experimentalists exploring new materials for organic electrodes.

Recent Development of Electrolytes for Aqueous Organic Redox Flow Batteries (Aorfbs): Current Status, Challenges, and Prospects

In recent years, aqueous organic redox flow batteries (AORFBs) have attracted considerable attention due to advancements in grid-level energy storage capacity research. These batteries offer remarkable benefits, including outstanding capacity retention, excellent cell performance, high energy density, and cost-effectiveness. The organic electrolytes in AORFBs exhibit adjustable redox potentials and tunable solubilities in water. Previously, various types of organic electrolytes, such as quinones, organometallic complexes, viologens, redox-active polymers, and organic salts, were extensively investigated for their electrochemical performance and stability. This study presents an overview of recently published novel organic electrolytes for AORFBs in acidic, alkaline, and neutral environments. Furthermore, it delves into the current status, challenges, and prospects of AORFBs, highlighting different strategies to overcome these challenges, with special emphasis placed on their design, composition, functionalities, and cost. A brief techno-economic analysis of various aqueous RFBs is also outlined, considering their potential scalability and integration with renewable energy systems.

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

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.

Density Functional Theory-Based Protocol to Calculate the Redox Potentials of First-row Transition Metal Complexes for Aqueous Redox Targeting Flow Batteries

Transition metal complexes are a promising class of redox mediators for targeting redox flow batteries due to the tunability of their electrochemical potentials. However, reliable time-efficient tools for the prediction of their reduction potentials are needed. In this work, we establish a suitable density functional theory protocol for their prediction using an initial experimental data set of aqueous iron complexes with bidentate ligands. The approach is then cross-validated using different complexes found in the redox-flow literature. We find that the solvation model affects the prediction accuracy more than the functional or basis set. The smallest errors are obtained using the COSMO-RS solvation model (mean average error (MAE)=0.24 V). With implicit solvation models, a general deviation from experimental results is observed. For a set of similar ligands, they can be corrected using simple linear regression (MAE=0.051 V for the initial set of iron complexes).

Computation of Oxidation Potentials of Solvated Nucleobases by Static and Dynamic Multilayer Approaches

The determination of the redox properties of nucleobases is of paramount importance to get insight into the charge-transfer processes in which they are involved, such as those occurring in DNA-inspired biosensors. Although many theoretical and experimental studies have been conducted, the value of the one-electron oxidation potentials of nucleobases is not well-defined. Moreover, the most appropriate theoretical protocol to model the redox properties has not been established yet. In this work, we have implemented and evaluated different static and dynamic approaches to compute the one-electron oxidation potentials of solvated nucleobases. In the static framework, two thermodynamic cycles have been tested to assess their accuracy against the direct determination of oxidation potentials from the adiabatic ionization energies. Then, the introduction of vibrational sampling, the effect of implicit and explicit solvation models, and the application of the Marcus theory have been analyzed through dynamic methods. The results revealed that the static direct determination provides more accurate results than thermodynamic cycles. Moreover, the effect of sampling has not shown to be relevant, and the results are improved within the dynamic framework when the Marcus theory is applied, especially in explicit solvent, with respect to the direct approach. Finally, the presence of different tautomers in water does not affect significantly the one-electron oxidation potentials.

Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 data set with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large data set of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.