The increase in demand for more efficient gas turbine engines has driven the advancement of new materials and their specific requirements. Ceramic matrix composites (CMCs) have emerged as promising materials for aerospace applications due to their stability at high temperatures and their superior weight‐to‐thrust ratio compared to Ni‐based superalloys. Within the realm of CMCs, oxide‐based variants stand out for their exceptional oxidation resistance and thermo‐mechanical properties. While oxide‐based CMCs offer superior qualities at a lower manufacturing cost, their adoption remains rather limited in comparison to non‐oxide CMCs. This limitation stems from their higher thermal expansion coefficient and reduced operational temperature. This review delves into the processing techniques, material composition, and fiber architecture design of oxide‐based CMCs. Additionally, it explores their mechanical properties and investigates failure mechanisms such as fiber debonding, delamination, and fiber pull‐outs in various thermo‐mechanical environments to ascertain their stability across diverse applications. To widely comprehend the usage of oxide‐based CMCs in aerospace applications, this review thoroughly examines their corrosion behavior and delineates effective prevention methods. The corrosion characteristics of oxide‐based CMCs are critically evaluated, along with a nuanced exploration of their potential applications beyond aerospace, encompassing sectors like nuclear power and other industries.

This paper delves into the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in materials science, spotlighting their capability to expedite the discovery and development of newer, more efficient, and stronger compounds. It underscores the shift from traditional, resource-intensive approaches toward data-driven methodologies that leverage large datasets to predict properties, identify new materials, and optimize synthesis conditions with a satisfactory level of accuracy. Highlighting various techniques, including supervised, unsupervised, and reinforcement learning, alongside deep learning potential, the chapter presents case studies and applications ranging from predicting stress points in stochastic fields to optimizing thermal protection systems for spacecraft re-entry. It also explores the challenges and future directions, emphasizing the need for integrating experimental validations and developing tailored algorithms to overcome data and computational constraints. The narrative showcases ML and AI’s promise in revolutionizing material discovery, paving the way for innovative solutions in science and engineering.

ZrSi₂ was fabricated via reactive hot-pressing technique for 45 min at 1250°C and 1350°C, respectively. The hot-pressed sample exhibited Vickers hardness of 10.93 ± 0.32 GPa and indentation fracture toughness of 3.16 ± 0.54 MPa.m^1/2. Thermal conductivity (RT-2.99 W (m.K)⁻¹) and diffusivity (RT–18.2 mm² s⁻¹) of the sample decreased with increasing temperature due to phonon scattering. Oxidation studies were carried out for the hot-pressed samples from 1000°C to 1200°C for 5, 48 and 100 h in air. The oxidation test results showed that ZrSi₂ followed selective oxidation with Si diffusing into the oxide layers at high temperatures. At 1200°C, the surface of ZrSi₂ oxidised completely to form a high-temperature stable phase, zircon (ZrSiO₄). The autoclave test at 250°C and 250 bar shown negligible mass gain (∼0.022 g) which suggests the usage of ZrSi₂ for nuclear fuel cladding applications.