AI-driven design of multifunctional nanomaterials in revolutionizing high-temperature, high-power solutions for space technology: potentials, challenges and perspectives

The extreme conditions of space-including intense thermal cycling, radiation, and micrometeoroid impacts-demand advanced materials that surpass the capabilities of conventional alloys and composites. This paper highlights the transformative potential of integrating artificial intelligence (AI) with multifunctional nanomaterials to overcome these challenges and revolutionize space technology. While nanomaterials like carbon nanotubes (CNTs), graphene, and boron nitride nanotubes (BNNTs) offer exceptional thermal, mechanical, optical and radiation-shielding properties, their development has been hindered by vast design spaces, synthesis complexities, and a lack of data for extreme environments. This paper addresses these gaps by hypothetically investigating how AI-driven methodologies for property prediction and multi-objective optimization can accelerate the discovery and optimization of next-generation nanomaterials. Key findings demonstrate that AI-guided design enables the creation of materials with tailored functionalities, including thermal interface materials with conductivities exceeding 200 W/m K, radiation-tolerant magnetic alloys with 50% less demagnetization, and self-cooling optical coatings maintaining high reflectivity after long thermal cycles. These advancements facilitate significant system-level improvements, such as mass reduction in shielding and thruster-specific impulse increase. The paper concludes that a synergistic AI-nanomaterial approach is essential to meet the escalating demands of future space exploration, though challenges in data scarcity, high- temperature modeling, and scalable manufacturing remain. Prioritizing hybrid AI-physics models and international collaboration for standardized testing is recommended to fully realize this potential.