Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl) Enabled by Machine Learning Interatomic Potentials.

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl molten salt across varied thermodynamic conditions ( = 473-613 K and = 2.

Ternary nanostructure of CdS-rGO-Ag for enhanced photoelectrochemical application.

In the present study, we have synthesized the ternary nanostructure of CdS-rGO-Ag by using the solvothermal method for the enhanced photocatalytic as well as electrocatalytic activity of the material. The optical properties of the prepared samples were characterized by using UV-visible spectroscopy and photoluminescence (PL) spectroscopy. A decline in the energy gap of CdS-rGO-Ag nanostructure to 1.

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile.

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only.

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides.

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann-Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides.