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Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data.

sorbed Pd II . All potentials are reported versus SCE (saturated calomel electrode), although for the scanning tunneling microscopy (STM) experiments a platinum wire was used as a pseudo-reference electrode (E Pt = +0.55 ± 0.05 V vs. SCE). Details about the experimental setup for cyclic voltammetry and STM are described elsewhere [26]. After their preparation, the samples for the photoemission experiments were immediately stored under an argon atmosphere, reducing the exposure time to ambient conditions during their loading into the ultrahigh-vacuum system of the electron spectrometer to less than a minute.The photoelectron spectra were measured with a Fisons Escalab-210 system using monochromatic Al Ka radiation (1486.6 eV, spot size < 1 mm). All spectra were taken with an overall energy resolution (full width at half maximum) of 0.36 eV and an angular resolution of ± 12 to avoid photoelectron-diffraction effects. To calibrate the instrument, the Fermi edge of a gold reference sample was determined, defining the origin of the binding energy scale (E B = 0). It has to be emphasized that radiation damage to the SAM by the X-ray exposure could be avoided due to the small-spot X-ray source, allowing a certain sample position to be probed for times of less than 1 h, followed by moving to a new position. During that time, no measurable decreases in the corresponding core-level intensities, or changes in their line shapes, were detected.

Thermal conductivity of two-dimensional (2D) materials is of interest for energy storage, nanoelectronics and optoelectronics. Here, we report that the thermal conductivity of molybdenum disulfide can be modified by electrochemical intercalation. We observe distinct behaviour for thin films with vertically aligned basal planes and natural bulk crystals with basal planes aligned parallel to the surface. The thermal conductivity is measured as a function of the degree of lithiation, using time-domain thermoreflectance. The change of thermal conductivity correlates with the lithiation-induced structural and compositional disorder. We further show that the ratio of the in-plane to through-plane thermal conductivity of bulk crystal is enhanced by the disorder. These results suggest that stacking disorder and mixture of phases is an effective mechanism to modify the anisotropic thermal conductivity of 2D materials.

An extremely low operating electric field has been achieved on zinc oxide (ZnO) nanowire field emitters grown on carbon cloth. Thermal vaporization and condensation was used to grow the nanowires from a mixture source of ZnO and graphite powders in a tube furnace. An emission current density of 1 mA/ cm 2 was obtained at an operating electric field of 0.7 V / m. Such low field results from an extremely high field enhancement factor of 4.11ϫ 10 4 due to a combined effect of the high intrinsic aspect ratio of ZnO nanowires and the woven geometry of carbon cloth.