Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields.

Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF-NaF-KF (FLiNaK).

Product Distribution from Precursor Bite Angle Variation in Multitopic Alkyne Metathesis: Evidence for a Putative Kinetic Bottleneck.

In the dynamic synthesis of covalent organic frameworks and molecular cages, the typical synthetic approach involves heuristic methods of discovery. While this approach has yielded many remarkable products, the ability to predict the structural outcome of subjecting a multitopic precursor to dynamic covalent chemistry (DCC) remains a challenge in the field. The synthesis of covalent organic cages is a prime example of this phenomenon, where precursors designed with the intention of affording a specific product may deviate dramatically when the DCC synthesis is attempted.

Impact of Shape Persistence on the Porosity of Molecular Cages.

Porous materials provide a plethora of technologically important applications that encompass molecular separations, catalysis, and adsorption. The majority of research in this field involves network solids constructed from multitopic constituents that, when assembled either covalently or ionically, afford macromolecular arrangements with micro- or meso-porous apertures. Recently, porous solids fabricated from discrete organic cages have garnered much interest due to their ease of handling and solution processability.