Significant progress has been made in recent years in theoretical modeling of the electric
double layer (EDL), a key concept in electrochemistry important for energy storage …

Thermal energy storage (TES) is increasingly important due to the demand-supply
challenge caused by the intermittency of renewable energy and waste heat dissipation to …

The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

Vehicle electrification has been proven to be an efficient way to reduce carbon dioxide
emissions and solve the energy crisis. Lithium-ion batteries (LiBs) are considered the …

Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

This work provides a data-oriented overview of the rapidly growing research field covering
machine learning (ML) applied to predicting electrochemical corrosion. Our main aim was to …

With the continuous advancement and exploration of science and technology, the future
trend of energy technology will be the deep integration of digitization, networking …

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …