We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Molten salts are important thermal conductors used in molten salt reactors and solar
applications. To use molten salts safely, accurate knowledge of their thermophysical …

Redox flow batteries (RFBs) are a promising technology for stationary energy storage
applications due to their flexible design, scalability, and low cost. In RFBs, energy is carried …

High-temperature molten salt research is undergoing somewhat of a renaissance these
days due to the apparent advantage of these systems in areas related to clean and …

We developed a deep potential machine learning model for simulations of chemical
reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using …

Redox-active organic materials are emerging as the new playground for the design of new
exciting battery materials for rechargeable batteries because of the merits including …

Scalable electronic predictions are critical for soft materials design. Recently, the Electronic
Coarse-Graining (ECG) method was introduced to renormalize all-atom quantum chemical …

Fluoride salts are prospective materials for applications in some next-generation nuclear
reactors and their thermophysical properties at various conditions are of interest …

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the
spatiotemporal limitations of ab initio simulations, but major challenges remain in their …

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by
researchers to analyze the crystallographic structures of materials in functional devices (eg …