MAX phases, a family of ternary layered carbide and nitride compounds characterized by their atomic-scale hybridization of metallic and covalent-ionic bonding, have emerged as potential materials for extreme environments, including fusion reactor cladding and ultrahigh-temperature sensing. Despite a twofold increase in known compositions over the past five years, the discovery and application of novel MAX phases remain hindered by metastable phase competition under non-equilibrium synthesis, inefficiencies in experimental synthesis/characterization, and ambiguous performance metrics under extreme conditions (e.g., high temperatures, irradiation). Recent breakthroughs in computational materials science — notably high-throughput density functional theory (HT-DFT) and machine learning (ML) — have revolutionized the exploration of these materials by enabling predictive screening of stability and performance. This review systematically analyzes advances in theoretical understanding of MAX phases, focusing on three pillars: electronic structure, thermodynamics and irradiation performance. Finally, brief insights into the challenges and future opportunities for the MAX phases are provided.

A long-standing challenge in developing deep learning approaches has been the lack of high-quality datasets, especially in material and medical. Different from the lack of the scanning transmission electron microscope (STEM) images dataset in material, mammogram images can not to be simulated completely using mathematic or physics theory. Doctors can only diagnose the breast cancer in early detection with limited data. The scarcity and imbalance of mammogram dataset is the main reason led to various performance problems in computer-aided diagnosis (CAD) methods. Generate more high-quality mammograms is an immediate need of time. However, the wider augmentation of mammogram dataset has been hindered by the lack of method that synthesis high-resolution mammograms efficiently. To overcome this problem, we proposed an edge-guided progressive growing generative adversarial network (EPG-GAN) to efficiently synthesize high-resolution mammograms. By introducing the design of edge-guided into the EPG-GAN, the model is empowered to be able to avoid synthesizing unnatural images and thus generate mammograms effectively and more realistic. Turning progressive growing strategy as global training strategy, this design leverages the fusion block to fade the image trained by previous step in the next step smoothly and make sure that EPG-GAN can gradually generate mammograms from low to high resolution. To demonstrate the effectiveness of our proposed model, we conduct experiments on publicly available INbreast dataset. Qualitative and quantitative evaluations validate the performance of our model in synthesis high-resolution mammogram. The proposed EPG-GAN achieves 0.983 in structure similarity index measure (SSIM) score, which is significantly higher than other models. The results show that the EPG-GAN can effectively synthesis realistic and high-resolution mammograms, enabling the augmentation of the dataset and laying the foundation for enhancing CAD for breast cancer.

High-entropy carbide ceramics (HECCs) materials that are made up of more than four metal carbides, have great significances in the field of ultra-high temperature service environment because of their great thermal stability. Compared with single metal carbides, HECCs materials involve complicated combination of ingredients, multiple scale dimensions design and multi-field coupling service environment, accompanying with an inefficient developing by traditional empirical trial-and-error method. Fortunately, with the development of computational materials science, multi-scale simulation calculation methods improve the research and application efficiency of HECCs. This work briefly summarized the principle and calculation process of the representative first-principles calculations method, and then reviewed the usability in the estimation of composition stability, structural design and mechanical property of HECCs. Finally, the prospect of the first-principles calculation method in the study of HECCs was prospected.

It is undeniable that artificial intelligence (AI) and machine learning (ML) have become rooted at every level of our society. It is also true that engineers have always strived for improvement in design, materials, and manufacturing, the three cornerstones encapsulating most engineering challenges. Hence, it is no surprise that recent years saw a surge in engineering contributions employing AI and ML techniques. However, conversely to analytical models and finite element or finite volume analyses (FEA, FVA), and despite the countless pros, AI and ML models also present several cons. Trying to avoid a lengthy analysis of all discernable aspects, this perspective focuses on two specific prospects: one inward and one outward-oriented issue, each representing a weak point for ML approaches but also a challenge. On the one hand, ML models’ formulations are well-known and documented. However, to achieve reasonable accuracy, the quality and size of the training dataset is paramount, making its definition as important as the architecture of the ML model itself. On the other hand, the prediction accuracy outside of the latent space, and how to improve it, remains a huge question mark, and often a limitation to more traditional yet reliable approaches, such as analytical, FEA, and FVA modeling. At this point, two questions arise. The former: what are the criteria to define whether ML has an edge over conventional modeling approaches? While the latter: how to design ML models capable of being less of a black box and better performing outside of the latent space? Each question is addressed separately in a section of the paper, together with a summary of the available state-of-the-art and a commentary of the authors’ perspective on the matter.

Recent research on two-dimensional anion exchange membranes has highlighted the potential of MXene-based anion exchange membranes in advanced applications. This review provides a comprehensive summary of the preparation strategies for high-quality MXene materials, including methods such as hydrofluoric acid (HF) etching, electrochemical processes, hydrothermal synthesis, and artificial intelligence (AI)-assisted approaches. Various film-forming techniques, such as vacuum filtration, casting, electrospinning, and AI-driven neural network optimization, are also discussed for their role in producing uniform and stable MXene membranes. A detailed examination of interlayer spacing regulation reveals its critical influence on ion exchange membrane performance, particularly with regard to ion transport mechanisms, rates, pathways, selective permeability, and membrane stability. AI has emerged as a transformative tool in this domain, significantly enhancing material discovery and optimization processes by improving synthesis efficiency and tailoring properties for specific applications. The review further explores advanced strategies for interlayer spacing regulation, including surface functionalization, intercalation chemistry, composite formation with nanomaterials and polymers, and predictive modeling using neural networks. Beyond conventional applications in energy storage and catalysis, MXene materials demonstrate exceptional promise in AI-related fields due to their outstanding electrical conductivity, tunable surface chemistry, and mechanical flexibility. These properties position MXenes as key enablers for next-generation AI hardware systems, such as neuromorphic computing, intelligent sensing, and data storage. This work underscores the importance of integrating MXene research with AI to drive future advancements in both materials science and emerging technologies.