Improving ionic thermoelectrics: Using ions as charge carriers in thermoelectric devices usually requires using either thermal diffusion or redox reactions at two electrodes with different temperatures. Han et al. leveraged both of these strategies to develop a gelatin-based ionic thermoelectric device that uses alkali salts and an iron-based redox couple to generate a large thermopower. This device is capable of generating useful amounts of energy from body heat. Science , this issue p. 1091

Most crystalline inorganic materials, except for metals and some layer materials, exhibit bad flexibility because of strong ionic or covalent bonds, while amorphous materials usually display poor electrical properties due to structural disorders. Here, we report the simultaneous realization of extraordinary room temperature flexibility and thermoelectric performance in Ag₂Te_1–xS_x–based materials through amorphization. The coexistence of amorphous main phase and crystallites results in exceptional flexibility and ultralow lattice thermal conductivity. Furthermore, the flexible Ag₂Te_0.6S_0.4 glass exhibits a degenerate semiconductor behavior with a room temperature Hall mobility of ~750 cm² V⁻¹ s⁻¹ at a carrier concentration of 8.6 × 10¹⁸ cm⁻³, which is at least an order of magnitude higher than other amorphous materials, leading to a thermoelectric power factor also an order of magnitude higher than the best amorphous thermoelectric materials known. The in-plane prototype uni-leg thermoelectric generator made from this material demonstrates its potential for flexible thermoelectric device.

The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.