Aims & Scope

Applied Energy Science (AES) is an international, open-access, peer-reviewed journal dedicated to accelerating the translation of fundamental energy research into practical, scalable solutions for the real world. Positioned at the intersection of science, technology, and industry, the journal serves as a vital conduit between cutting-edge discoveries and their deployment in the global energy landscape. It is published quarterly online by Scilight Press.

The journal emphasizes high-impact, interdisciplinary research across a wide spectrum of topics, including—but not limited to—renewable energy systems, energy storage technologies, smart grids, hydrogen energy, carbon capture and utilization, and energy efficiency innovations. We particularly welcome contributions that demonstrate strong potential for industrial application, commercialization, or influence on policy and sustainable development.

Applied Energy Science (AES) is more than a platform for technical exchange—it is a catalyst for global energy transformation. We encourage submissions that provide actionable data, forward-thinking designs, and innovative engineering approaches that support the transition toward a cleaner, smarter, and more sustainable energy future. Topics of interest include, but are not limited to:

Energy Storage Technologies

electrochemical (battery-based) storage; thermal storage (molten salt storage, cryogenic energy storage, phase change materials, hydrogen storage ect;  

Energy Efficiency & Industrial Applications

smart energy management, industrial decarbonization technologies, electrification of industrial heat, ect

 Advanced Materials for Energy Applications

bio-based polymers; carbon-neutral or carbon-negative materials; aerogels; fuel cell materials; electrocatalysts for water splitting; piezoelectric materials; thermoelectric materials; photovoltaic materials; solid electrolytes; hydrogen storage materials ect;

 Renewable Energy Systems

bioenergy from waste, algae, or biomass; solar photovoltaics and system optimization; ect

 Energy Conversion Technologies:

fuel cells; hermoelectric generation, ect;

Energy Economics and Policy;

AI & Digital Technologies for Sustainable Energy Systems

AI-driven material discovery: Generative models for electrolyte/catalyst screening, molecular dynamics accelerated by machine learning; Predictive analytics: AI/ML-enhanced renewable energy forecasting, Prediction of failure and loss of control in the application of lithium-ion batteries and hydrogen energy systems;