Composite materials are increasingly required to operate in cryogenic environments, including liquid hydrogen and oxygen storage, deep-space structures, and polar infrastructures, where long-term strength, toughness, and reliability are essential. This review provides a unique contribution by systematically integrating recent advances in understanding cryogenic behaviour into a unified multi-scale framework. This framework synthesises four critical and interconnected aspects: constituent response, composite performance, enhancement mechanisms, and modelling strategies. At the constituent level, fibres retain stiffness, polymer matrices stiffen but embrittle, and nanoparticles offer tunable thermal and mechanical functions, which collectively define the system-level performance where thermal expansion mismatch, matrix embrittlement, and interfacial degradation dominate failure. The review further details toughening strategies achieved through nano-addition, hybrid fibre architectures, and thin-ply laminates. Modelling strategies, from molecular dynamics to multiscale finite element analysis, are discussed as predictive tools that link these scales, supported by the critical need for in situ experimental validation. The primary objective of this synthesis is to establish a coherent perspective that bridges fundamental material behaviour to structural reliability. Despite these advances, remaining challenges include consistent property characterisation at low temperature, physics-informed interface and damage models, and standardised testing protocols. Future progress will depend on integrated frameworks linking high-fidelity data, cross-scale modelling, and validation to enable safe deployment of next-generation cryogenic composites.

Predicting laboratory earthquakes using machine learning has progressed markedly recently. Previous related studies mainly focus on predicting the occurrence time and shear stress of laboratory earthquakes using acoustic emission signals. Here, based on numerical simulations, we use machine learning to show that statistical features of plate motion signals contain information about the slip duration and friction drop of laboratory earthquakes. We find that the plate motion signals during the initial slip stage contain the precursor information about the slip duration of laboratory earthquakes. However, to accurately predict the friction drop, we need to incorporate the plate motion signals during the entire slip stage. The results demonstrate that the high‐order moment and variance of plate motion signals are respectively among the best predictors for the slip duration and friction drop of laboratory earthquakes. Our work provides new insights for future investigations into natural earthquake prediction through machine learning.

Predicting laboratory earthquakes using machine learning has progressed markedly recently. Previous related studies mainly focus on predicting the occurrence time and shear stress of laboratory earthquakes using acoustic emission signals. Here, based on numerical simulations, we use machine learning to show that statistical features of plate motion signals contain information about the slip duration and friction drop of laboratory earthquakes. We find that the plate motion signals during the initial slip stage contain the precursor information about the slip duration of laboratory earthquakes. While to accurately predict the friction drop, we need to incorporate the plate motion signals during the entire slip stage. The results demonstrate that the high-order moment and variance of plate motion signals are respectively among the best predictors for the slip duration and friction drop of laboratory earthquakes. Our work provides new insights for future investigations into natural earthquake prediction through machine learning.

Cryogenic liquid hydrogen (LH ₂ ) and high‐pressure compressed gaseous hydrogen (CGH ₂ ) vessels are the core technologies for large‐scale hydrogen storage. This review systematically examines their design challenges and failure mechanisms under extreme cryogenic and high‐pressure conditions. For LH ₂ vessels, recent advances in thermal insulation, boil‐off gas (BOG) control, active thermal management and mitigation of cryogenic embrittlement in metallic and composite structures are highlighted. For CGH ₂ vessels, key strategies including fibre winding optimisation, nanoparticle reinforcement, gas‐tightness improvement and predictive modelling of burst, fatigue and impact failures are summarised. By identifying cryogenic and high‐pressure conditions as the two fundamental constraints, this study establishes a framework linking critical design issues with corresponding mitigation approaches. The review further identifies technical bottlenecks and outlines future research priorities, providing valuable insights into the development pathways of next‐generation hydrogen storage vessels for safe and efficient deployment.