Machine Learning-Driven Exploration of Composition- and Temperature-Dependent Transport and Thermodynamic Properties in LiF-NaF-KF Molten Salts for Nuclear Applications.

This study developed a high-precision deep potential (DP) model based on density functional theory (DFT) and the DP-GEN workflow to efficiently simulate the microscopic structures and thermophysical properties of LiF-NaF-KF molten salt systems with varying compositions. Through iterative optimization of the training data set using the DP-GEN active learning strategy, our DP model demonstrated excellent agreement with DFT calculations in predicting energies, forces, and stresses. Leveraging this model, we systematically investigated the local structures and properties of 22 FLiNaK molten salt compositions, including radial distribution functions (RDFs), coordination numbers (CNs), density (ρ), heat capacity (), self-diffusion coefficients (SDCs), electrical conductivity, and shear viscosity.

Antibacterial oxygenated ergostane-type steroids produced by the marine sponge-derived fungus sp.

Two oxygenated ergostane-type steroids including one new compound, 3-hydroxy-5,6-methoxyergosta-7,22-dien-15-one () along with a known analogue ergosta-6,22-dien-3,5,8-triol () were isolated from the crude extracts of the marine sponge-derived fungus sp. Their structures were elucidated on the basis of combined NMR and MS spectroscopic methods. Compound was a marine ergostane-type steroid with two methoxy groups at C-5 and C-6, respectively.