With the global energy system transitioning to renewable energy, high-efficiency energy storage and conversion technologies have become crucial. However, traditional research paradigms for the research and development (R&D) of energy materials such as batteries and electrocatalysts present the limitations in efficiency. This review systematically summarizes the progress of artificial intelligent (AI) in this field, ranging from classical machine learning (ML) to advanced representation methods such as graph neural networks (GNNs) and transformers that enable precise property prediction and structure generation. It also covers generative models for inverse design and large language models (LLMs) for knowledge extraction, along with key domain databases. Current challenges include limited interpretability and the underutilization of emerging AI technologies. Finally, this review discusses future directions such as the applications of multimodal language models, aiming to provide insights for accelerating high-performance energy materials innovation and advancing the global renewable energy transition.

ENGINEERING Energy is officially launched beginning with Issue 1, 2026. This milestone marks a strategic step to better reflect the journal’s expanded scope and its growing, diverse research community under the unified and prestigious ENGINEERINGjournal brand.

Key information is outlined below:

New title: ENGINEERING Energy

Effective from: Volume 20 (2026)

Continuity: Volume and issue numbering will continue sequentially, and all previously published content will remain fully accessible

ISSN:3091-5023 (Online) / 3091-5015 (Print)

Abstracted/Indexed in: SCI, EI, CAS, Scopus, INSPEC, Google Scholar, and other major databases

ENGINEERING Energy remains firmly committed to publishing high-quality, impactful research across the full spectrum of energy science and engineering. We sincerely thank the research community for its continued support and warmly welcome future submissions under the journal’s new title.

Journal homepage:https://link.springer.com/journal/11708

Publishing model:Hybrid

Journal Impact Factor：6.2 (2024)

Submission to first decision (median):30 days

A research team led by Prof. Seunguk Song from the Department of Energy Science at Sungkyunkwan University (SKKU), in collaboration with the Institute for Basic Science (IBS), the University of Pennsylvania, and the U.S. Air Force Research Laboratory, has published a comprehensive technical roadmap for two-dimensional (2D) Indium Selenides (InSe)—a key material for next-generation low-power and quantum computing.

A variety of proteins extracted from rice milling byproducts were shown to provide different qualities desired in plant-based cheesemaking, including firm texture and meltability. Mahfuzur Rahman, a food scientist and grain processing engineer with the Arkansas Agricultural Experiment Station, and graduate student Ruslan Mehadi Galib published their results in the journal Future Foods under the title "Three shads of plant protein from a single rice cultivar: Insights into subunit profiles, molecular structures, functional and nutritional properties, and cheesemaking performance."