Electrochemical energy conversion and storage have attracted widespread interest as green and sustainable technologies. In particular, research on water electrolysis and supercapacitors (SCs) has experienced significant growth, focusing on novel electrodes/electrocatalysts with prominent performances. Recently, computational frameworks employing machine learning (ML) algorithms have revitalized the targeted design of advanced nanomaterials as electrodes/electrocatalysts with tunable electronic configurations and superior reactivity. Descriptor-based analysis has proven efficient in elucidating the structure–property (e.g., activity, selectivity, and stability) relationships, addressing the complex interactions between the catalytic surface and reactant species and predicting enormous data sets. In this contribution, we present an overview of ML-driven electrode/electrocatalyst design, highlighting several novel algorithms and descriptors. The latest advancements in ML approaches are presented to efficiently screen a wide range of metal-based materials. Leveraging recent achievements, this review describes the application of ML for the discovery of active and durable nanomaterials, including identifying active sites, manipulating compositions at the atomic level, predicting the structure/performance, and optimizing thermodynamic properties as well as kinetic barriers. Moreover, recent milestones and state-of-the-art progress in ML integration strategies-materials informatics to stimulate the design of highly efficient electrode/electrocatalyst systems for the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and SCs are discussed. Finally, we highlight potential future directions for uncovering the revolutionary potential of ML in boosting sustainability and prediction efficiency in the electrochemical energy conversion and storage sector. This review intends to reinforce the junctions between industry and academia and merge endeavors from fundamental understanding to technological execution.

In this study, we investigate the crystal structure, surface energy, and atomic arrangement of Cu₂O. Understanding these properties is crucial for exploring the potential applications and understanding the behavior of this material. We employ the Wulff construction method and an artificial neural network (ANN) model to analyze the relative surface energies of different crystal facets and predict the surface energy of Cu₂O. The ANN model exhibits excellent performance, demonstrating its effectiveness in predicting material properties and providing automated feature-learning and nonlinear-modeling capabilities. Moreover, we analyze the atomic arrangements on various crystal facets and observe the presence of oxygen atoms on the {100} facet, as well as exposed under-coordinated copper atoms on the {111} and {110} facets. High-index facets such as {211} exhibit a higher atomic step density and screw dislocation density. By precisely controlling the synthesis process, it is possible to manipulate the proportion of high-index facets. These findings highlight the significance of understanding the surface energy and atomic arrangement of Cu₂O crystals for comprehending their properties and surface reactions. In summary, this study provides valuable insights into the crystal structure, surface energy, and atomic arrangement of Cu₂O, offering inspiration for its properties and potential applications. The combination of the Wulff construction method and ANN modeling provides a comprehensive understanding of Cu₂O crystals and their surface behavior, contributing to the field of materials science and laying the foundation for various future applications utilizing the unique properties of Cu₂O.

A database of rutile TiO ₂ containing 3000 morphologies was established. With this database, the surface energy was predicted from the experimentally observed crystal equilibrium morphology using the KNN model.

This chapter explores the utilization of artificial neural network (ANN) models in predicting surface energy values. ANN models are a type of machine learning (ML) algorithm inspired by the way the human brain processes information. The chapter delves into the theoretical foundations of ANN models and their application in modeling surface energy, a crucial parameter in various scientific and industrial processes. By training the ANN models with relevant datasets, researchers can develop a predictive model capable of estimating surface energy values with high accuracy. The chapter discusses the methodology, challenges, and potential benefits of using an ANN-based approach for surface energy prediction, offering insights into the intersection of artificial intelligence and materials science.

To enhance the corrosion resistance, adhesion strength, and electrical conductivity of bipolar plates for proton exchange membrane fuel cells (PEMFCs), a duplex surface treatment comprising plasma nitriding (PN) followed by arc ion plating of a TiN coating was applied to 316L stainless steel. The duplex coating exhibited a bilayer structure with a TiN top layer (~1.3 μm) and a nitrided diffusion layer (~9.2 μm). Compared with a single TiN coating, it showed superior corrosion resistance in both cathode and anode environments, with corrosion current densities of 0.069 and 0.422 μA·cm⁻², respectively, and maintained stable performance over 10,000 s of potentiostatic polarization. Its interfacial contact resistance at 140 N·cm⁻² was 7.84 mΩ·cm², below the Department of Energy (DOE) 2025 target of 10 mΩ·cm². The water contact angle also increased, benefiting water management. The nitrided interlayer substantially enhanced the adhesion strength of the TiN coating. These findings confirm that the sequential combination of plasma nitriding and TiN coating is an effective strategy for improving the corrosion resistance, electrical conductivity, and adhesion of stainless steel bipolar plates for PEMFC applications.