Themed collection Artificial Intelligence & Machine Learning in Energy Storage & Conversion

11 items
Open Access 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.

Graphical abstract: Artificial intelligence and machine learning in energy storage and conversion
Open Access 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).

Graphical abstract: Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials
From the themed collection: Energy Advances Recent Review Articles
Open Access 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.

Graphical abstract: Machine learning in energy chemistry: introduction, challenges and perspectives
From the themed collection: Energy Advances Recent Review Articles
Open Access 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.

Graphical abstract: Machine learning-inspired battery material innovation
From the themed collection: Energy Advances Recent Review Articles
Open Access 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.

Graphical abstract: 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
Open Access 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.

Graphical abstract: Lithium dynamics at grain boundaries of β-Li3PS4 solid electrolyte
Open Access 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.

Graphical abstract: Enhancing glucose classification in continuous flow hydrothermal biomass liquefaction streams through generative AI and IR spectroscopy
From the themed collection: SDG 7: Affordable and clean energy
Open Access 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.

Graphical abstract: Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data
Open Access 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.

Graphical abstract: Physics-informed Gaussian process regression of in operando capacitance for carbon supercapacitors
From the themed collection: Supercapacitors– Topic Highlight
Open Access 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.

Graphical abstract: Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning
Open Access 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.

Graphical abstract: Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products
11 items

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.

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