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RESEARCH PAPER · Current Opinion in Electrochemistry · 2024

A brief overview of deep generative models and how they can be used to discover new electrode materials

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A brief overview of deep generative models and how they can be used to discover new electrode materialsOriginalPrototyping with Generative AI Design to Debottleneck Conventional Design: Incorporation on Proton Exchange Membrane Electrolyzer2Entropy-informed weighting channel normalizing flow for deep generative models3Spin Matters: A Multidisciplinary Roadmap to Understanding Spin Effects in Oxygen Evolution Reaction During Water Electrolysis13AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model6Generative AI Empowers Brain-Computer Interfaces: A Review-Perspective on Technical Realities and Future Visions3Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics4Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning1Artificial catalyst generation for the oxygen reduction reaction using conditional variational autoencoder and atomistic calculations2

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  1. Prototyping with Generative AI Design to Debottleneck Conventional Design: Incorporation on Proton Exchange Membrane Electrolyzer

    Sustainable Energy Technologies and Assessments · 2025-08-11

    Juin Yau Lim, C. T. Aisyah Sarjuni, Bee Huah Lim, Sze Ney Chan, Dominic C.Y. Foo, Adrian Chun Minh Loy, Wai Yin Wong, ChangKyoo Yoo

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  2. Entropy-informed weighting channel normalizing flow for deep generative models

    Pattern Recognition · 2025-09-16

    Wei Chen, Shian Du, Shigui Li, Delu Zeng, John William Paisley

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  3. Spin Matters: A Multidisciplinary Roadmap to Understanding Spin Effects in Oxygen Evolution Reaction During Water Electrolysis

    Advanced Energy Materials · 2025-10-10

    Emma van der Minne, Priscila Vensaus, Vadim Ratovskii, Seenivasan Hariharan, Jan Behrends, Cesare Franchini, Jonas Fransson, Sarnjeet S. Dhesi, Felix Gunkel, Florian Gossing, Georgios Katsoukis, Ulrike I. Kramm, Magalı́ Lingenfelder, Qianqian Lan, Yury V. Kolen’ko, Yang Li, Ramsundar Rani Mohan, Jeffrey McCord, Lingmei Ni, Eva Pavarini, Rossitza Pentcheva, David H. Waldeck, Michael Verhage, Anke Yu, Zhichuan Jason Xu, Piero Torelli, Silvia Mauri, Narcis Avarvari, Anja Bieberle‐Hütter, Christoph Baeumer

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  4. AI driven electrochemical interface design: Development and economic evaluation of low-cost electrode materials based on the 'structure-activity consumption' model

    Journal of environmental chemical engineering · 2025-11-01

    Tingfa Zhou, Peng Gui, Chao Hu, Weihua Xiao, Dong Liu, Zhiping Wang

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  5. Generative AI Empowers Brain-Computer Interfaces: A Review-Perspective on Technical Realities and Future Visions

    IEEE Transactions on Consumer Electronics · 2026-01-05

    Shuqiang Wang, Yi Guo, Yihang Dong, Yanyan Shen, Zhiguo Zhang, Albert C. Cheung, Jiangbo Pu, Sheng-hua Zhong, Raymond Kai‐Yu Tong, Ye Li, Michael K. Ng, Kim-Fung Tsang, Guanhua Ren

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  6. Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics

    Energy storage materials · 2026-01-12

    Quanquan Zhang, Quanquan Zhang, Mingyu Yang, Guanxi Sun, Yue Xiang, Shitong Wang, Junying Zhang, Shuangqi Li

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  7. Material Discovery, Renewable Energy, and the Scope of Shallow Learning and Deep Learning

    Advances in computational intelligence and robotics book series · 2026-02-19

    Himadri Sekhar Das, Heranmoy Maity, Sudipta Banerjee, Maitrayee Chakrabarty, Gourisankar Roymahapatra, Banibrata Bag

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  8. Artificial catalyst generation for the oxygen reduction reaction using conditional variational autoencoder and atomistic calculations

    npj Computational Materials · 2026-04-14

    Taishiro Wakamiya, Atsushi Ishikawa

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