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https://www.selleckchem.com/products/AZD2281(Olaparib).html Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been applied to an array of standard (Arrhenius and super-Arrhenius) glass formers, where work on "soft spots" indicates a connection between the linear vibrational response of a configuration and the energy barriers to non-linear deformations. Here we study the Voronoi model, which takes its inspiration from dense epithelial monolayers and which displays anomalous, sub-Arrhenius scaling of its dynamical relaxation time with decreasing temperature. Despite these differences, we find that the likelihood of rearrangements can nevertheless vary by several orders of magnitude within the model tissue and extract a local structural quantity, "softness," that accurately predicts the temperature dependence of the relaxation time. We use an information-theoretic measure to quantify the extent to which softness determines impending topological rearrangements; we find that softness captures nearly all of the information about rearrangements that is obtainable from structure, and that this information is large in the solid phase of the model and decreases rapidly as state variables are varied into the fluid phase.In comparison with the prevalent 2D material-supported single atom catalysts (SACs), the design and fabrication of SACs with single molecule substrates are still challenging. Here we introduce a new type of SAC in which a recently identified all-boron fullerene B40 is employed as the support and its catalytic performance toward the nitrogen reduction reaction (NRR) process is explored in theory. Taking advantage of the novel heptagonal ring substructure on the sphere and the electron-deficient nature of boron, the atomic metals are facile to reside on B40 to form atomically dispersed η7-B40M exohedr

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