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.
View Article and Find Full Text PDFInorganic sulfide solid-state electrolytes, especially LiPSX (X = Cl, Br, I), are considered viable materials for developing all-solid-state batteries because of their high ionic conductivity and low cost. However, this class of solid-state electrolytes suffers from structural and chemical instability in humid air environments and a lack of compatibility with layered oxide positive electrode active materials. To circumvent these issues, here, we propose LiMAsSI (M=Si, Sn) as sulfide solid electrolytes.
View Article and Find Full Text PDFIon transport in solids is a key topic in solid-state ionics. It is critical but challenging to understand the relationship between material structures and ion transport. Nanochannels in crystals provide ion transport pathways, which are responsible for the fast ion transport in fast lithium (Li)-ion conductors.
View Article and Find Full Text PDFAn intramolecular formal [3+2] cycloaddition of activated aziridines and epoxides with electron-deficient alkene has been developed for the general and efficient construction of bridged aza- and oxa-[.2.1] ( = 3 or 4) skeletons.
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