LiNi_0.5Mn_1.5O₄ (LNMO) spinel has been extensively investigated as one of the most promising high-voltage cathode candidates for lithium-ion batteries. The electrochemical performance of LNMO, especially its rate performance, seems to be governed by its crystallographic structure, which is strongly influenced by the preparation methods. Conventionally, LNMO materials are prepared via solid-state reactions, which typically lead to microscaled particles with only limited control over the particle size and morphology. In this work, we prepared Ni-doped LiMn₂O₄ (LMO) spinel via the polyol method. The cycling stability and rate capability of the synthesized material are found to be comparable to the ones reported in literature. Furthermore, its electronic charge transport properties were investigated by local electrical transport measurements on individual particles by means of a nanorobotics setup in a scanning electron microscope, as well as by performing DFT calculations. We found that the scarcity of Mn³⁺ in the LNMO leads to a significant decrease in electronic conductivity as compared to undoped LMO, which had no obvious effect on the rate capability of the two materials. Our results suggest that the rate capability of LNMO and LMO materials is not limited by the electronic conductivity of the fully lithiated materials.

Data-driven discovery workflow of aza-aromatic class of electroactive compounds for efficient energy storage in aqueous redox flow batteries.

Quinones are one of the most promising and widely investigated classes of redox active materials for organic aqueous redox flow batteries. However, quinone-based flow batteries still lack the necessary performance in terms of metrics, such as specific capacity, power density, and long-term stability, to achieve mass market adoption. These performance metrics are directly related to the physicochemical properties of the quinone molecules, including their equilibrium redox potential, aqueous solubility, and chemical stability. Given the enormous chemical and configurational space of possible quinones and the high tunability of their properties, there has been a recent surge in the use of high-throughput virtual screening (HTVS) for the rational design and discovery of new high-performing molecules. In this review article, HTVS efforts for the computational design and discovery of quinones are reviewed with a special focus on the enumerated space of core quinone motif, the methods and approximations used for the estimation of performance descriptors, and the emergent structure-property relationships. The knowledge and methodological gaps in conventional HTVS efforts are discussed, and strategies for improvement are suggested.

Organic cathode materials are attractive candidates for the development of high-performance Li-ion batteries (LIBs). The chemical space of candidate molecules is too vast to be explored solely by experiments; however, it can be systematically explored by a high-throughput computational search that incorporates a spectrum of screening techniques. Here, we present a time- and resource-efficient computational scheme that incorporates machine learning and semi-empirical quantum mechanical methods to study the chemical space of approximately 200 000 quinone-based molecules for use as cathode materials in LIBs. By performing an automated search on a commercial vendor database, computing battery-relevant properties such as redox potential, gravimetric charge capacity, gravimetric energy density, and synthetic complexity score, and evaluating the structural integrity upon the lithiation process, a total of 349 molecules were identified as potentially high-performing cathode materials for LIBs. The chemical space of the screened candidates was visualized using dimensionality reduction methods with the aim of further downselecting the best candidates for experimental validation. One such directly purchasable candidate, 1,4,9,10-anthracenetetraone, was analyzed through cyclic voltammetry experiments. The measured redox potentials of the two lithiation steps, , of 3.3 and 2.4 V, were in good agreement with the predicted redox potentials, , of 3.2 and 2.3 V vs. Li/Li⁺, respectively. Lastly, to lay out the principles for rational design of quinone-based cathode materials beyond the current work, we constructed and discussed the quantitative structure property relationships of quinones based on the data generated from the calculations.

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.