Conventional cathodes of Li-ion batteries mainly operate through an insertion-extraction process involving transition metal redox. These cathodes will not be able to meet the increasing requirements until lithium-rich layered oxides emerge with beyond-capacity performance. Nevertheless, in-depth understanding of the evolution of crystal and excess capacity delivered by Li-rich layered oxides is insufficient. Herein, various in situ technologies such as X-ray diffraction and Raman spectroscopy are employed for a typical material Li Ni Mn O , directly visualizing O O (peroxo oxygen dimers) bonding mostly along the c-axis and demonstrating the reversible O /O redox process. Additionally, the formation of the peroxo OO bond is calculated via density functional theory, and the corresponding OO bond length of ≈1.3 Å matches well with the in situ Raman results. These findings enrich the oxygen chemistry in layered oxides and open opportunities to design high-performance positive electrodes for lithium-ion batteries.

The effective mass is a convenient descriptor of the electronic band structure used to characterize the density of states and electron transport based on a free electron model. While effective mass is an excellent first-order descriptor in real systems, the exact value can have several definitions, each of which describe a different aspect of electron transport. Here we use Boltzmann transport calculations applied to ab initio band structures to extract a density-of-states effective mass from the Seebeck Coefficient and an inertial mass from the electrical conductivity to characterize the band structure irrespective of the exact scattering mechanism. We identify a Fermi Surface Complexity Factor: $${N}_{{\rm{v}}}^{\ast }{K}^{\ast }$$ N v * K * from the ratio of these two masses, which in simple cases depends on the number of Fermi surface pockets $$({N}_{{\rm{v}}}^{\ast })$$ ( N v * ) and their anisotropy K*, both of which are beneficial to high thermoelectric performance as exemplified by the high values found in PbTe. The Fermi Surface Complexity factor can be used in high-throughput search of promising thermoelectric materials.

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We present an overview and preliminary analysis of computed thermoelectric properties for more than 48 000 inorganic compounds from the Materials Project (MP). We compare our calculations with available experimental data to evaluate the accuracy of different approximations in predicting thermoelectric properties. We observe fair agreement between experiment and computation for the maximum Seebeck coefficient determined with MP band structures and the BoltzTraP code under a constant relaxation time approximation (R 2 = 0.79). We additionally find that scissoring the band gap to the experimental value improves the agreement. We find that power factors calculated with a constant and universal relaxation time approximation show much poorer agreement with experiment (R 2 = 0.33). We test two minimum thermal conductivity models (Clarke and Cahill-Pohl), finding that both these models reproduce measured values fairly accurately (R 2 = 0.82) using parameters obtained from computation. Additionally, we analyze this data set to gain broad insights into the effects of chemistry, crystal structure, and electronic structure on thermoelectric properties. For example, our computations indicate that oxide band structures tend to produce lower power factors than those of sulfides, selenides, and tellurides, even under the same doping and relaxation time constraints. We also list families of compounds identified to possess high valley degeneracies.Finally, we present a clustering analysis of our results. We expect that these studies should help guide and assess future high-throughput computational screening studies of thermoelectric materials.