Material descriptors for predicting thermoelectric performance

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On the tuning of electrical and thermal transport in thermoelectrics: an integrated theory–experiment perspective (2016, background): “In this regard, more accurate descriptors are needed to nail down the right composition.(192) On the experimental side, very little work is done currently on highthroughput screening.”
Theory-Guided Machine Learning in Materials Science (2016, unknown): “A similar recognition of the underlying physics yielded a descriptor for carrier mobility in thermoelectrics based on bulk modulus and band effective mass (Yan et al., 2015).”
Effective mass and Fermi surface complexity factor from ab initio band structure calculations (2017, background): “1 and 2, or within a particular energy window.(2) We think of effective valley degeneracy, Nv, as describing the effective number carrier pockets contributing to conduction, where the contribution may only be partial if that pocket is displaced by more than ~kBT from the chemical potential (μ).”