A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage

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.

RedDB, a computational database of electroactive molecules for aqueous redox flow batteries

An increasing number of electroactive compounds have recently been explored for their use in high-performance redox flow batteries for grid-scale energy storage. Given the vast and highly diverse chemical space of the candidate compounds, it is alluring to access their physicochemical properties in a speedy way. High-throughput virtual screening approaches, which use powerful combinatorial techniques for systematic enumerations of large virtual chemical libraries and respective property evaluations, are indispensable tools for an agile exploration of the designated chemical space. Herein, RedDB: a computational database that contains 31,618 molecules from two prominent classes of organic electroactive compounds, quinones and aza-aromatics, has been presented. RedDB incorporates miscellaneous physicochemical property information of the compounds that can potentially be employed as battery performance descriptors. RedDB’s development steps, including: (i) chemical library generation, (ii) molecular property prediction based on quantum chemical calculations, (iii) aqueous solubility prediction using machine learning, and (iv) data processing and database creation, have been described.

Measurement(s)	Clean Database • Physicochemical properties of redox active molecules • aqueous solubility
Technology Type(s)	Calculation • Physics • machine learning

Theoretical Protocol Based on Long-Range Corrected Density Functional Theory and Tuning of Range-Split Parameter for Two-Electron Two-Proton Reduction of Phenylazocarboxylates

A theoretical protocol based on long-range corrected density functional theory is suggested for a highly accurate estimation of the two-electron two-proton (2e2p) reduction potential of ethyl 2-phenylazocarboxylate derivatives. Geometry optimization and single-point energy refinement with ωB97X-D are recommended. The impact of polarization and diffusion functions in the basis sets on the 2e2p reduction potential is discussed. Further improvements can be achieved by tuning the range-split parameter based on the linear relationship between the Hammett constant of phenyl substituents and the optimal ω value that most accurately reproduces the experiments. The suggested protocol can accurately predict the 2e2p reduction potential of five ethyl 2-phenylazocarboxylate derivatives. Based on these findings, 22 additional candidates are suggested to enlarge the electrochemical window and to increase the selectivity of 2e2p reactions. This study contributes to the development of a theoretical approach to accurately estimate the 2e2p reduction potential of azo groups.

Platyphylloside, a potential inhibitor from epicarp of B.aegyptiaca against CYP450 protein in T.rubrum – In vitro and in silico approaches

Trichophyton rubrum is one of the major disease causing pathogens in human; mainly it causes tinea pedis, tinea cruris and tinea corporis. Cytochrome P450 which considered to be an important protein that can impact ergosterol biosynthesis pathway. B. aegyptiaca is rich source of secondary metabolites with tremendous medicinal values and it has sweet pulp, leaves with spine, strong seed and oily kernel. The epicarp of the fruit was taken for this study to inhibit T. rubrum using in vitro and in silico techniques. The epicarp portion was extracted using various solvents and water. The anti-dermatophytic activity on T. rubrum of these extracts was assessed utilizing poison plate technique with 5 individual concentrations. The fractioned chloroform extract of epicarp had fully inhibited the growth of T. rubrum at 3 mg/ml. Further, the chloroform extract was subjected to LC-MS analysis, in total, 40 compounds were elucidated. Then, the derived compounds were included for predicting ADMETox properties using Qikprop module. From the analysis 40 compounds were identified to be eligible for docking process. Then the desirable compounds, drug Ketoconazole were subjected to docking analysis using Glide module of Schrödinger. It shows that Platyphylloside has better docking result than other compounds and drug Ketoconazole. Further, MD simulation was carried out for Ketoconazole-Cyp450 and Platyphylloside-CYP450 complexes using Desmond, Schrödinger. MD simulation study also confirmed that the Platyphylloside-CYP450 complex more stable. This study suggests that Platyphylloside may act as potential inhibitor and it could be further subjected to experimental analysis to inhibit the T. rubrum growth.

Evaluation of Computational Chemistry Methods for Predicting Redox Potentials of Quinone-Based Cathodes for Li-Ion Batteries

High-throughput computational screening (HTCS) is an effective tool to accelerate the discovery of active materials for Li-ion batteries. For the evaluation of organic cathode materials, the effectiveness of HTCS depends on the accuracy of the employed chemical descriptors and their computing cost. This work was focused on evaluating the performance of computational chemistry methods, including semi-empirical quantum mechanics (SEQM), density-functional tight-binding (DFTB), and density functional theory (DFT), for the prediction of the redox potentials of quinone-based cathode materials for Li-ion batteries. In addition, we evaluated the accuracy of three energy-related descriptors: (1) the redox reaction energy, (2) the lowest unoccupied molecular orbital (LUMO) energy of reactant molecules, and (3) the highest occupied molecular orbital (HOMO) energy of lithiated product molecules. Among them, the LUMO energy of the reactant compounds, regardless of the level of theory used for its calculation, showed the best performance as a descriptor for the prediction of experimental redox potentials. This finding contrasts with our earlier results on the calculation of quinone redox potentials in aqueous media for redox flow batteries, for which the redox reaction energy was the best descriptor. Furthermore, the combination of geometry optimization using low-level methods (e.g., SEQM or DFTB) followed by energy calculation with DFT yielded accuracy as good as the full optimization of geometry using the DFT calculations. Thus, the proposed calculation scheme is useful for both the optimum use of computational resources and the systematic generation of robust calculation data on quinone-based cathode compounds for the training of data-driven material discovery models.