Electrochemical energy technologies are central to the net-zero transition, yet their multiscale characteristics ranging from atomic materials to device architectures present critical challenges in research and development (R&D). Although artificial intelligence (AI) has accelerated discovery and design in this field, commonly used predictive AI methods remain limited in enabling disruptive advances. Generative AI has shown transformative potential in disciplines such as biology and medicine, though its impact on electrochemical energy R&D is only beginning to emerge. Here we review recent progress in applying generative AI to molecular and crystal discovery, electrode microstructure design, and system optimization, with particular attention to the role of large language models in electrochemical engineering. We argue for a paradigm shift toward generative electrochemical intelligence (GenE), a physics-informed, multimodal framework that integrates human expertise with automated experimentation. We anticipate that GenE will redefine the R&D paradigm for rapidly deployable electrochemical energy technologies and accelerate their translation into real-world applications.
This talk will outline how AI models, when trained on large datasets of experimental electrochemical results, can address the limitations of traditional approaches in autonomous labs, and discuss the potential of this methodology to overcome current limitations in scaling up autonomous laboratories for broader energy t...
This review examines emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows.
Jaehwan Choi, Seongmin Kim, Junkil Park et al.· Chemical Reviews· 4 citations
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computati...
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices.
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim et al.· Advanced Intelligent Systems· 0 citations
This review provides a structured overview of AI applications in nanotechnology, including data-driven nanomaterial discovery and inverse design using machine learning (ML) and generative models, optimization of nanoparticle synthesis through Bayesian optimization and self-driving laboratories, targeted drug delivery a...
Two‐Stage Material Screening (TSMS) is developed, an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts.
Xueyu Hu, Yucun Zhou, Haoyu Li et al.· Advances in Materials· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
