We present an investigation of the phase stability, electrochemical stability and Li + conductivity in the Li 10±1 MP 2 X 12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of but at the expense of reduced electrochemical stability. We also studied the effect of lattice parameter changes on the Li + conductivity and found the same asymmetry in behavior between increases and decreases in the lattice parameters, i.e., decreases in the lattice parameters lower the Li + conductivity significantly, while increases in the lattice parameters increase the Li + conductivity only marginally. Based on these results, we conclude that the size of the S 2− is near optimal for Li + conduction in this structural framework.

In this work, we investigated the effect of Rb and Ta doping on the ionic conductivity and stability of the garnet Li 7+2x−y (La 3−x Rb x )(Zr 2−y Ta y )O 12 (0 ≤ x ≤ 0.375, 0 ≤ y ≤ 1) superionic conductor using first principles calculations. Our results indicate that doping does not greatly alter the topology of the migration pathway, but instead acts primarily to change the lithium concentration. The structure with the lowest activation energy and highest room temperature conductivity is Li 6.75 La 3 Zr 1.75 Ta 0.25 O 12 (E a = 19 meV, σ 300K = 1 × 10 −2 S cm −1 ). All Ta-doped structures have significantly higher ionic conductivity than the undoped cubic Li 7 La 3 Zr 2 O 12 (c-LLZO, E a = 24 meV, σ 300K = 2 × 10 −3 S cm −1 ). The Rb-doped structure with composition Li 7.25 La 2.875 Rb 0.125 Zr 2 O 12 has a lower activation energy than c-LLZO, but further Rb doping leads to a dramatic decrease in performance. We also examined the effect of changing the lattice parameter at fixed lithium concentration and found that a decrease in the lattice parameter leads to a rapid decline in Li + conductivity, whereas an expanded lattice offers only marginal improvement. This result suggests that doping with larger cations will not provide a significant enhancement in performance. Our results find higher conductivity and lower activation energy than is typically reported in the experimental literature, which suggests that there is room for improving the total conductivity in these promising materials.

First-principles calculations have been used to investigate the effects of Al and Mg doping on the prevention of degradation phenomena in Li(NiCoMn)O cathode materials. Specifically, we have examined the effects of dopants on the suppression of oxygen evolution and cation disordering, as well as their correlation. It is found that Al doping can suppress the formation of oxygen vacancies effectively, while Mg doping prevents the cation disordering behaviors, i.e., excess Ni and Li/Ni exchange, and Ni migration. This study also demonstrates that formation of oxygen vacancies can facilitate the construction of the cation disordering, and vice versa. Delithiation can increase the probabilities of formation of all defect types, especially oxygen vacancies. When oxygen vacancies are present, Ni can migrate to the Li site during delithiation. However, Al and Mg doping can inhibit Ni migration, even in structures with preformed oxygen defects. The analysis of atomic charge variations during delithiation demonstrates that the degree of oxidation behavior in oxygen atoms is alleviated in the case of Al doping, indicating the enhanced oxygen stability in this structure. In addition, changes in the lattice parameters during delithiation are suppressed in the Mg-doped structure, which suggests that Mg doping may improve the lattice stability.

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The discovery of high-performance functional materials is crucial for overcoming technical issues in modern industries. Extensive efforts have been devoted toward accelerating and facilitating this process, not only experimentally but also from the viewpoint of materials design. Recently, machine learning has attracted considerable attention, as it can provide rational guidelines for efficient material exploration without time-consuming iterations or prior human knowledge. In this regard, here we develop an inverse design model based on a deep encoder-decoder architecture for targeted molecular design. Inspired by neural machine language translation, the deep neural network encoder extracts hidden features between molecular structures and their material properties, while the recurrent neural network decoder reconstructs the extracted features into new molecular structures having the target properties. In material design tasks, the proposed fully data-driven methodology successfully learned design rules from the given databases and generated promising light-absorbing molecules and host materials for a phosphorescent organic light-emitting diode by creating new ligands and combinatorial rules.

Computationally predicting reverse intersystem crossing (RISC) rates is important for designing new thermally activated delayed fluorescence (TADF) materials. We report a method that can quantitatively predict RISC rates by explicitly considering the spin–vibronic coupling mechanism. The coupling element of the spin–vibronic Hamiltonian is obtained by expanding the spin–orbit and the non-Born–Oppenheimer terms to second order and is then brought into the Golden Rule rate under the Condon approximation. The rate equation is solved directly in the time domain using a correlation function approach. The contributions of the first-order direct spin–orbit coupling and the second-order spin–vibronic coupling to an RISC rate can be quantitatively analyzed in a separate manner. We demonstrate the utility of the method by applying it to a representative TADF system, where we observe that the spin–vibronic portion is substantial but not dominant especially with a relatively small triplet–singlet energy gap. Likewise, our method may elucidate the physical background of efficient nonradiative transitions from the lowest triplet to a higher lying singlet in other purely organic TADF systems, and it will be of great utility toward designing new such molecules.

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Developing electrode materials with high-energy densities is important for the development of lithium-ion batteries. Here, we demonstrate a mesoporous molybdenum dioxide material with abnormal lithium-storage sites, which exhibits a discharge capacity of 1,814 mAh g−1 for the first cycle, more than twice its theoretical value, and maintains its initial capacity after 50 cycles. Contrary to previous reports, we find that a mechanism for the high and reversible lithium-storage capacity of the mesoporous molybdenum dioxide electrode is not based on a conversion reaction. Insight into the electrochemical results, obtained by in situ X-ray absorption, scanning transmission electron microscopy analysis combined with electron energy loss spectroscopy and computational modelling indicates that the nanoscale pore engineering of this transition metal oxide enables an unexpected electrochemical mass storage reaction mechanism, and may provide a strategy for the design of cation storage materials for battery systems.