We are delighted to announce that the Energy Advances themed issue on Artificial Intelligence & Machine Learning for Energy Storage and Conversion is now online.
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
- From materials, to devices, to systems, with an emphasis on how AI and ML have accelerated research and development in these fields.
Read the full issue online: https://rsc.li/EnergyAdvancesAIML
It includes:
Machine learning in energy chemistry: introduction, challenges and perspectives
Yuzhi Xu, Jiankai Ge and Cheng-Wei Ju
Energy Adv., 2023, Advance Article, DOI: 10.1039/D3YA00057E
Machine learning-inspired battery material innovation
Man-Fai Ng, Yongming Sun and Zhi Wei Seh
Energy Adv., 2023, 2, 449-464, DOI: 10.1039/D3YA00040K
Carlotta L.M von Meyenn and Stefan Palkovits
Energy Adv., 2023,2, 691-700, DOI: 10.1039/D2YA00312K
Haruka Tobita, Yuki Namiuchi, Takumi Komura, Hiroaki Imai, Koki Obinata, Masato Okada, Yasuhiko Igarashi and Yuya Oaki
Energy Adv., 2023, Accepted Manuscript, DOI: 10.1039/D3YA00161J
Physics-informed Gaussian process regression of in operando capacitance for carbon supercapacitors
Runtong Pan, Mengyang Gu and Jianzhong Wu
Energy Adv., 2023, Advance Article, DOI: 10.1039/D3YA00071K
Zachary Gariepy, Guiyi Chen, Anni Xu, Zhuole Lu, Zhi Wen Chen and Chandra Veer Singh
Energy Adv., 2023,2, 410-419, DOI: 10.1039/D2YA00316C
We hope that you enjoy reading this collection of articles. Please get in touch if you have any questions about this themed collection or want to contribute to the growing work on artificial intelligence and machine learning in energy sciences.