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). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. This work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.

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. As such, rational design principles are needed to accelerate discovery in cage synthesis using DCC. Herein, we test the hypothesis that precursor bite angle contributes significantly to the energy landscape and product distribution in multitopic alkyne metathesis (AM). By subjecting a series of precursors with varying bite angles to AM, we experimentally demonstrate that the product distribution, and convergence toward product formation, is strongly dependent on this geometric attribute. Surprisingly, we discovered that precursors with the ideal bite angle (60°) do not afford the most efficient pathway to the product. The systematic study reported here illustrates how seemingly minor adjustments in precursor geometry greatly affect the outcome of DCC systems. This research illustrates the importance of fine-tuning precursor geometric parameters in order to successfully realize desirable targets.