Editorial
Artificial intelligence and machine learning in energy storage and conversion
Zhi Weh Seh, Kui Jiao and Ivano Castelli introduce the Energy Advances themed issue on Artificial intelligence and machine learning in energy storage and conversion.
Perspective
Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials
With increased awareness of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) can facilitate fast development of high-performance electrochemical energy storage systems (EESSs).
Review Article
Machine learning in energy chemistry: introduction, challenges and perspectives
This review explores machine learning's role in energy chemistry, spanning organic photovoltaics, perovskites, catalysis, and batteries, highlighting its potential to accelerate eco-friendly, sustainable energy development.
Review Article
Machine learning-inspired battery material innovation
Data-driven machine learning is a proven technique for battery material discovery and enables the development of sustainable next-generation batteries.
Communication
Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations
Machine learning analysis revealed the importance of structural features involving A-site metals in AxByOz multimetal oxides for their OER activity.
Paper
Lithium dynamics at grain boundaries of β-Li3PS4 solid electrolyte
The lithium diffusivity behavior at the grain boundaries of β-Li3PS4 solid electrolytes is strongly dependent on the grain boundary type and the degree of disorder.
Paper
Enhancing glucose classification in continuous flow hydrothermal biomass liquefaction streams through generative AI and IR spectroscopy
Energy from fossil fuels is forecasted to contribute to 28% of the energy demand by 2050.
Paper
Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data
A capacity prediction model for organic anode active materials was constructed using sparse modeling for small data. The new model was validated in terms of the prediction accuracy, validity of the descriptors, and amount of the training data.
Paper
Physics-informed Gaussian process regression of in operando capacitance for carbon supercapacitors
Modeling electric double layer (EDL) capacitance with physics-informed Gaussian process regression (PhysGPR) avoids unphysical predictions that might be encountered in conventional machine learning methods.
Paper
Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning
A method to use the concept of directed evolution to synthesize new catalysts for the oxidative coupling of methane (OCM) in silico via a combination of a genetic algorithm and machine learning (ML) is described.
Paper
Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products
CO2RR binary alloy catalyst design insight gained through density functional theory and machine learning with a focus on COCOH adsorption energy.
About this collection
Artificial intelligence (AI) and machine learning (ML) are transforming the way we perform scientific research in recent years. Guest edited by Prof. Zhi Wei Seh (A*STAR, Singapore); Prof. Kui Jiao (Tianjin University); Dr. Ivano Castelli (Technical University of Denmark), this themed collection welcomes papers that demonstrate the implementation of AI and ML in energy storage and conversion, including batteries, supercapacitors, electrocatalysis, and photocatalysis. Work can range from materials, to devices, to systems, with an emphasis on how AI and ML have accelerated research and development in these fields.