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A magnetically separatable catalyst Fe3O4@SiO2@PEI@Au (gold) nanoparticle was successfully constructed by a novel regional selective photoreduction method. Based on the photolysis mechanism of a type II photoinitiator, through controlling the distribution of polyethylene imine (PEI), Au nanoparticles about 10 nm, which are only on the surface of the Fe3O4@SiO2@PEI nanoparticle, could be photoreduced due to the PEI acting as a coordinating agent, capping agent, and photoreducing agent simultaneously. The small size Au nanoparticles endow the catalyst with a high catalytic performance toward the reduction of 4-nitroaniline to 4-aminophenol by NaBH4. In addition, magnetic Fe3O4@SiO2@PEI@Au nanoparticles could easily be recovered and could be reused at least six times still keeping catalytic efficiency higher than 95%, which contributes to their high stability and magnetization. Furthermore, compared to another reported approach, this method showed great regional selectivity of reducing metal nanoparticles by controlling the distribution of the PEI. Taking advantage of the regional selectivity of the photoreducing method could also be used to fabricate other metal nanoparticles as catalysts for various reactions.

A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. Based on our developed DP model, the low-lying structures of 101 different sized Al clusters are extensively searched, among which the lowest-energy candidates of 69 sized clusters are updated. Our calculations demonstrate that machine-learning is indeed powerful in generating potentials to describe the interaction of atoms in complex materials.

In hybrid perovskites, the organic molecules and inorganic frameworks exhibit distinct static and dynamic characteristics. Their coupling will lead to fascinating phenomena, such as large polarons, dynamic Rashba–Dresselhaus effects, etc. In this paper, deep potential molecular dynamics (DPMD) is employed, a large‐scale MD simulation scheme with DFT accuracy, to study hybrid perovskites formamidinium lead iodide (FAPbI3) and methylamonium lead iodide (MAPbI3). A spontaneous hybrid nano‐domain behavior, namely multiple molecular rotation nano‐domains embedded into a single [PbI6]4− octahedra rotation domain, is first discovered at low temperatures. The behavior originates from the interplay between the long range order of molecular rotation and local lattice deformation, and clarifies the puzzling structural features of FAPbI3 at low temperatures. The work provides new insights into the structural characteristics and stability of hybrid perovskite, as well as new ideas for the structural characterization of organic–inorganic coupled systems.

The non-thermal effect of microwave was evaluated quantitatively by the calculation of activation energy and pre-exponential factors with an isothermal microwave.

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