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.
The development of self-driving laboratories in electrochemistry represents a transformative shift in how energy storage materials are discovered and optimized. While conventional AI models in autonomous labs have largely relied on computational data, significant challenges remain in bridging the gap between simulation-driven predictions and real-world experimental outcomes. This talk will explore a novel approach: leveraging AI models trained on experimental data to improve the accuracy and reliability of autonomous laboratory systems, with a particular focus on energy storage electrochemical materials.
Conventional AI models often rely heavily on computational simulations, which may not fully capture the complexities and nuances of experimental data. In contrast, models trained directly on high-throughput experimental results can better account for electrochemical behavior under real-world conditions, improving predictive power and guiding more effective material discovery processes. By integrating experimental data from diverse electrochemical analysis techniques, such as cyclic voltammetry, impedance spectroscopy, and battery cycling tests, AI can identify key trends, optimize experimental workflows, and accelerate the discovery of novel materials for energy storage 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. Through case studies, we will demonstrate how this data-driven approach can enhance the efficiency of material screening, reduce the time and resources needed for experimentation, and provide actionable insights into optimizing energy storage electrochemical materials. Furthermore, we will discuss the potential of this methodology to overcome current limitations in scaling up autonomous laboratories for broader energy technology applications.
By combining the power of AI with high-throughput experimental data, this approach holds the potential to revolutionize the discovery and optimization of electrochemical materials for energy storage, driving forward a more sustainable and energy-efficient future.
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· 1 citation
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 computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
Artificial intelligence (AI) is transforming the way materials are designed, understood, and manufactured. 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. We outline ongoing strategies to embed AI across the materials design workflow—from synthesis and characterization to large‐scale simulations enabled by machine learning techniques and approaching ab initio accuracy—and discuss key challenges that remain on the path toward intelligent (bio)materials discovery.
Cristiano Malica, Kostya S. Novoselov, Seongmin Kim et al.· Advanced Intelligent Systems· 0 citations
The convergence of artificial intelligence (AI) and nanotechnology has substantially transformed the discovery, design, synthesis, characterization and biomedical application of nanomaterials. 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 and personalized nanomedicine enabled by predictive ML models, deep learning for nanoscale imaging, spectroscopy and real-time particle tracking, AI-accelerated simulation of nanofluids and complex nanosystems, together with the emerging challenges, opportunities and future directions of AI-driven nanotechnology. We discuss the capabilities and limitations of widely used AI methods, including artificial neural networks, random forests, support vector machines, reinforcement learning, generative adversarial networks, variational autoencoders, graph neural networks and large language models, highlighting their suitability for different nanotechnology applications. In addition, the review examines key challenges that currently limit broader translation of AI-enabled nanotechnologies, including limited availability of standardized high-quality datasets, model interpretability, reproducibility, validation across independent datasets and regulatory considerations. Finally, we discuss emerging research directions, including autonomous experimentation, multiscale AI frameworks, AI-assisted nanorobotic systems and closed-loop therapeutic platforms, emphasizing that these represent promising future opportunities requiring further technological development and rigorous experimental and clinical validation.
Oxygen reduction and evolution reactions (ORR and OER) are key electrochemical processes central to energy conversion and chemical transformation. However, the inherently complex, multi-physics nature of ORR/OER-together with diverse operating environments-poses significant challenges to the rational design of electrocatalysts based on structure-property relationships. To overcome these challenges, we developed Two-Stage Material Screening (TSMS), 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. Demonstrated in protonic solid oxide cells (P-SOCs), TSMS screened 6,940,032 compositions and identified top-performing candidates that were experimentally validated, achieving a peak power density of 2.68 W cm-2 in fuel cell mode and a current density of 3.51 A cm-2 at 1.3 V in electrolysis mode, with stable performance maintained over 500 h at 600°C. Our analysis revealed that electron affinity is strongly associated with thermodynamic stability, while d-p hybridization and densification resistance emerge as the primary descriptors governing electrocatalytic activity. By combining predictive modeling with mechanistic understanding, TSMS establishes a versatile and broadly generalizable paradigm for accelerating materials discovery.
Xueyu Hu, Yucun Zhou, Haoyu Li et al.· Advances in Materials· 0 citations