Copper Nitrate-Mediated Selective Bond Cleavage of Alkynes:Diverse Synthesis of Isoxazoles

An unprecedented copper nitrate-mediated bond cleavage of alkynes was developed for the modular synthesis of isoxazoles,where either C—S bond or C≡C triple bond was cleaved selectively.Substituents attached to the C≡C triple bonds could differentiate the chemical bonds cleavage and reaction pathways disparately.Various transformations of products illustrate promising applications of the given protocols.

Rotational motion of polyanion versus volume effect associated with ionic conductivity of several solid electrolytes

Volume effect has been extensively investigated in several families of solid electrolytes, i.e., expanding the skeleton lattice by bigger-size substitution favors the ionic conduction. However, this effect is not applicable in α-Li2SO4 and α-Na3PO4 based inorganic ionic plastic crystal electrolytes, a unique family of solid electrolytes. Here, it is proposed that the underlying rotational motion effect of polyanion, which is actually inhibited by the substitution of bigger-size polyanion in single-phase solid solution region and causes the unexpected lowering of the ionic conductivity instead, should need the more consideration. Furthermore, through utilizing the rotational motion effect of polyanion, it is given that a new explanation of the ionic conductivities of Li10MP2S12 （M = Si, Ge, Se） electrolytes deviating from the volume effect. These results inspire new vision of rationalization of the high-performance solid electrolytes by tuning the rotational motion effect of polyanion.

Active learning for the power factor prediction in diamond-like thermoelectric materials

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.