Linear correlation models for the redox potential of organic molecules in aqueous solutions

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.

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.

John PC. Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries

Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.

Machine learning for flow batteries: opportunities and challenges

With increased computational ability of modern computers, the rapid development of mathematical algorithms and the continuous establishment of material databases, artificial intelligence (AI) has shown tremendous potential in chemistry. Machine learning (ML), as one of the most important branches of AI, plays an important role in accelerating the discovery and design of key materials for flow batteries (FBs), and the optimization of FB systems. In this perspective, we first provide a fundamental understanding of the workflow of ML in FBs. Moreover, recent progress on applications of the state-of-art ML in both organic FBs and vanadium FBs are discussed. Finally, the challenges and future directions of ML research in FBs are proposed.

Directed Design of a AuI Complex with a Reduced Mesoionic Carbene Radical Ligand: Insights from 1,2,3-Triazolylidene Selenium Adducts and Extensive Electrochemical Investigations

Carbene-based radicals are important for both fundamental and applied chemical research. Herein, extensive electrochemical investigations of nine different 1,2,3-triazolylidene selenium adducts are reported. It is found that the half-wave potentials of the first reduction of the selones correlate with their calculated LUMO levels and the LUMO levels of the corresponding triazolylidene-based mesoionic carbenes (MICs). Furthermore, unexpected quasi-reversibility of the reduction of two triazoline selones, exhibiting comparable reduction potentials, was discovered. Through UV/Vis/NIR and EPR spectroelectrochemical investigations supported by DFT calculations, the radical anion was unambiguously assigned to be triazoline centered. This electrochemical behavior was transferred to a triazolylidene-type MIC-gold phenyl complex resulting in a MIC-radical coordinated AuI species. Apart from UV-Vis-NIR and EPR spectroelectrochemical investigations of the reduction, the reduced gold-coordinated MIC radical complex was also formed in situ in the bulk through chemical reduction. This is the first report of a monodentate triazolylidene-based MIC ligand that can be reduced to its anion radical in a metal complex. The results presented here provide design principles for stabilizing radicals based on MICs.