Achieving global decarbonization goals by 2050 demands rapid progress in renewable energy and energy storage and conversion technologies. Artificial Intelligence (AI) and Machine Learning (ML) are increasingly used in Materials Science, enabling faster and more efficient discovery, characterization, and optimization of energy storage and conversion materials. Drawing on insights from the 2024 MRS Energy Sustainability Panel, this perspective explores how AI/ML is reshaping the energy materials landscape, from predictive modeling and microstructural analysis to autonomous laboratories and closed-loop experimentation. It highlights six key areas of progress and challenge, including data infrastructure, sustainability of AI itself, and the need for interdisciplinary collaboration, and it argues that the rate-limiting step is now the evidence rather than the algorithm: claims of AI-accelerated discovery should be assessed on data readiness, model uncertainty, experimental validation, autonomy level, and life-cycle benefit. By integrating literature examples and expert viewpoints, the article presents an overview of how AI and ML can accelerate innovation in batteries, catalysts, and emerging material systems, while emphasizing the importance of open data, reproducibility, and responsible deployment to ensure long-term impact and sustainability. Artificial intelligence (AI) is accelerating the discovery of advanced materials for batteries, fuel cells, and other clean-energy technologies. However, the reliability and practical value of AI-driven materials discovery depend critically on the quality of the underlying data, the robustness of model predictions, and experimental validation. We propose a framework for evaluating AI-driven materials discoveries based on five key considerations: data quality, predictive uncertainty, experimental synthesis and validation, degree of automation, and the energy and environmental costs associated with AI-driven approaches. Together, these criteria provide a basis for assessing not only whether an AI system can identify promising materials, but also whether the resulting discoveries are reproducible, experimentally validated, and environmentally meaningful.
Reliable battery state-of-health (SOH) assessment for electric vehicles (EVs) remains difficult in real-world operation because charging telemetry is noisy, irregularly sampled, and often incomplete. This paper presents a practical framework toward real-world EV battery SOH assessment and introduces HuberTV2 as a physics-informed degradation filtering module within that framework. HuberTV2 combines Huber loss, irregular-time second-order total variation regularization, and aging-informed bounds on degradation and apparent recovery to extract a smooth, rate-bounded effective-energy proxy trajectory. Evaluated on longitudinal charging telemetry from 39 EVs against four baseline methods, HuberTV2 achieves the highest adjacent-pair monotonicity (95.2 κ =0.376 ) and 95
Two-dimensional (2D) transition metal dichalcogenide (TMDC) nanosheets have attracted considerable attention as promising noble-metal-free electrocatalysts for the hydrogen evolution reaction (HER). Their catalytic performance is governed by structural and electronic heterogeneities, including edges, defects, strain, and dopants. Therefore, understanding the relationship between the structure and HER activity is essential for the rational design of advanced TMDC electrocatalysts. Scanning electrochemical cell microscopy (SECCM) has emerged as a powerful technique for the direct visualization of local HER activity. This review summarizes the recent advances in SECCM studies on HER electrocatalysis using TMDC nanosheets. We first introduce pioneering studies that established local HER imaging on representative TMDC electrocatalysts. We then review recent studies by categorizing them based on two principal approaches to improving HER activity: increasing the density of active sites and enhancing their intrinsic catalytic activity, while highlighting the specific contributions of SECCM in each case. Finally, we highlight recent applications of SECCM to emerging 2D material architectures that extend beyond conventional TMDC nanosheets. This review illustrates how SECCM has evolved from a local electrochemical imaging technique into an indispensable platform for the rational design of next-generation 2D TMDC electrocatalysts. This review summarizes scanning electrochemical cell microscopy (SECCM) studies of local hydrogen evolution reaction (HER) activity on two-dimensional transition metal dichalcogenide (TMDC) nanosheets. Given the strong structural dependence of the hydrogen evolution reaction (HER) activity on two-dimensional (2D) transition metal dichalcogenide (TMDC) nanosheets, how can their local structure–HER activity relationships and underlying reaction mechanisms be fully elucidated? How can the structure-dependent HER activity spatially resolved by scanning electrochemical cell microscopy (SECCM) be translated into rational design principles for developing more active and efficient 2D TMDC electrocatalysts? How can SECCM be integrated with correlative microscopy, spectroscopy, and complementary electrochemical techniques to obtain chemical, electronic, and dynamic information beyond activity mapping and thereby provide a deeper mechanistic understanding of HER?
Harvesting ubiquitous low-grade waste heat has remained challenging, and ionic thermoelectrics (i-TEs) have shown greater promise than electronic thermoelectrics. The i-TEs are of two types: (1) redox reaction-based i-TEs and (2) Soret effect-driven i-TEs. This work summarizes recent advances in Soret effect-driven i-TEs, which generate a voltage due to differences in the thermophoretic mobilities of electrolyte ions when the i-TE is subjected to a temperature gradient, with the cold end at room temperature. Unlike electronic thermoelectrics, Soret effect-driven i-TEs exhibit a high thermopower of several mV/K. We correlate the thermopower generated by such Soret effect-driven i-TEs to (1) the various entropic contributions of the electrolyte, and (2) electrode porosity. We highlight the role of electrode porosity in driving novel electrochemical voltage oscillations, which can open new avenues for i-TE applications. Notably, we present proof-of-concept demonstrations of Soret effect-driven i-TEs and discuss various schemes employed by researchers to continuously generate power—a hot discussion topic in the field of i-TEs. Lastly, we compare the efficiencies of reported i-TEs and propose future directions to guide the research community in Soret effect-driven i-TEs. In this invited review article, we summarize the (1) developments in electrolyte discovery for Soret effect-driven i-TEs and link them to their entropic contributions, (2) role of electrode porosity in governing thermopower and voltage oscillations, (3) different continuous operation schemes, (4) proof-of-concept demonstrations reported to date, and (5) future research directions.
Performing electrolytic water splitting in acid can enable a step change in electrolyser technology, providing lower overpotential reactions and opportunities for membrane-free electrolysers. Decoupled acid electrolysis exploits H+ intercalation and pseudocapacitive reactions in transition metal oxides to temporally split the hydrogen and oxygen evolution reactions. While the performance of the transition metal oxide is critical to the overall efficiency of the decoupled electrolysis reactions, the role of the carbon support has not been explored. This is surprising as proton battery literature has shown the capacity of these supports to store H+ ions via similar intercalation reactions. Here, bio-derived activated carbons (AC) from Alder charcoal and Birch wood are prepared and compared with commercial conductive carbon additives for decoupled electrolysis in 0.5 M H2SO4. The use of bio-derived activated carbons increases the pseudocapacitive performance by more than 280
