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DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.
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The efficient removal of alkyne impurities for the production of polymer-grade lower olefins remains an important and challenging goal for many industries. We report a strategy to control the pore interior of faujasite (FAU) zeolites by the confinement of isolated open nickel(II) sites in their six-membered rings. Under ambient conditions, Ni@FAU showed remarkable adsorption of alkynes and efficient separations of acetylene/ethylene, propyne/propylene, and butyne/1,3-butadiene mixtures, with unprecedented dynamic separation selectivities of 100, 92, and 83, respectively. In situ neutron diffraction and inelastic neutron scattering revealed that confined nickel(II) sites enabled chemoselective and reversible binding to acetylene through the formation of metastable [Ni(II)(C2H2)3] complexes. Control of the chemistry of pore interiors of easily scalable zeolites has unlocked their potential in challenging industrial separations.
RESUMO
A novel artificial anion chemosensor 1 based on 2,2'-di(4-nitrophenylurea-ß-N-yl)-1, 1'-binaphthyl is designed and synthesized for sensing anions including halide ions and oxoanions. The fluorescent emission of the binaphthyl of receptor 1, forming the hydrogen bonding with anions as the sensing mechanism, is monitored in DMSO for detecting anions. In brief, while most of the anion chemosensors are switch-off fluorescent chemosensor, or non-fluorescent sensor, receptor 1 exhibits obviously the switch-on emission during the complexation with H(2)PO(4)(-).
