AI-Driven Material Discovery for Advanced Spacesuit Engineering

Abstract

The development of spacesuit materials is a critical challenge in space exploration, requiring advanced materials that can withstand extreme temperatures, intense radiation, and mechanical stress. Traditional material discovery relies on time-intensive experimentation and trial-and-error synthesis, slowing down innovation and increasing costs. This study explores the potential of Artificial Intelligence (AI) in revolutionizing material selection by analyzing resonance structures and isomeric configurations to predict and optimize key molecular properties.

Using supervised machine learning models, particularly Decision Tree Regression and Random Forest Regression, this research demonstrates that AI can accurately predict tensile strength, thermal resistance, and radiation shielding capacity based on molecular descriptors. The models achieved 99.8% accuracy in predicting material durability and identified resonance energy and isomer type as critical factors influencing performance. AI-driven predictive modeling accelerates the identification of high-performance polymers and composites, significantly reducing the time and cost of material discovery.

Despite its promise, AI-based material selection faces challenges such as limited training data, simulation-reality gaps, and the need for experimental validation. Future research should focus on expanding material databases, integrating reinforcement learning, and validating AI-selected materials in simulated space environments. This study highlights how AI can reshape spacesuit engineering, paving the way for materials that enhance astronaut safety and enable long-term space missions.