Radical paths: The title reaction of olefins with NaSO2CF3 and N‐hydroxy‐N‐phenylacetamide at room temperature is described for the first time (see scheme). This reaction provides a practical and convenient route to a series of trifluoromethylated alcohols bearing a wide range of functional groups.

Balancing the second-harmonic generation (SHG) coefficient, band gap, and birefringence is a vital but addressable challenge for designing infrared nonlinear optical materials. By applying a “rigidity–flexibility coupling” strategy, a quaternary diamond-like phosphide, Mg2In3Si2P7, with wurtzite-type superstructure was successfully designed and synthesized. Remarkably, it achieved the rare coexistence of giant second-harmonic generation (2 × ZnGeP2 and 7.1 × AgGaS2), suitable band gap (2.21 eV), moderate birefringence (0.107), and wide IR transparent range (0.56–16.4 μm). First-principles calculations revealed that the giant SHG response and large birefringence can be attributed to the synergy of arrangement-aligned [InP4] and [SiP4] tetrahedra. This work not only opens a new avenue for designing advanced infrared nonlinear optical materials but also may spur more explorations on quaternary diamond-like pnictides.

We consider the problem of learning distributed representations for entities and relations of multi-relational data so as to predict missing links therein. Convolutional neural networks have recently shown their superiority for this problem, bringing increased model expressiveness while remaining parameter efficient. Despite the success, previous convolution designs fail to model full interactions between input entities and relations, which potentially limits the performance of link prediction. In this work we introduce ConvR, an adaptive convolutional network designed to maximize entity-relation interactions in a convolutional fashion. ConvR adaptively constructs convolution filters from relation representations, and applies these filters across entity representations to generate convolutional features. As such, ConvR enables rich interactions between entity and relation representations at diverse regions, and all the convolutional features generated will be able to capture such interactions. We evaluate ConvR on multiple benchmark datasets. Experimental results show that: (1) ConvR performs substantially better than competitive baselines in almost all the metrics and on all the datasets; (2) Compared with stateof-the-art convolutional models, ConvR is not only more effective but also more efficient. It offers a 7% increase in MRR and a 6% increase in Hits@10, while saving 12% in parameter storage.

Hydrogen-bonded organic frameworks (HOFs) with intrinsic, tunable, and uniform pores are promising candidate of membrane for molecular separation but have yet to be explored in this filed. Herein, a type...