Few machine learning models consider materials' multi-property prediction, such as the density of states. We propose a novel machine learning model, called Mat2Spec, for predicting ab initio phonon and electronic density of states for crystalline materials.
Mobile excitons in metals have been elusive, as screening usually suppresses their formation. Here, the authors demonstrate such mobile bound states in quasi-one-dimensional metallic TaSe₃, taking advantage of its low dimensionality and carrier density.
Many layered materials (clays, Mxenes) exhibit gradual change in d(001) spacing us a function of humidity with increments smaller than size of water molecule. This effect is not related to size of “permeation channels” in GO membranes according to common structural model of GO hydration.
We design and synthesize a bifunctional molecular additive to fabricate reduced-dimensional perovskites with a more monodispersed quantum well thickness distribution and passivated surfaces. We report as a result bright perovskite LEDs with narrowband emission and a high EQE of 25.6%.
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