Leveraging Large Language Models to Predict Atomic Oxygen Erosion of Polymers in Low Earth Orbit

Abstract

Low Earth orbit (LEO), home to most active satellites, is projected to undergo a debris-fragmentation cascade known as Kessler Syndrome. In light of this eventuality, it is imperative to maximize the operational lifetime of LEO satellites. Instrumental to this goal is the development of next-generation polymeric materials with improved resistance to atomic oxygen (AO) erosion. Because genuine LEO exposure experiments are prohibitively expensive and LEO conditions are difficult to reproduce in the laboratory, data-driven approaches offer a promising alternative for accelerating materials discovery. Here, we develop a fine-tuned large language model (LLM) based on OpenAI's GPT-4o to predict the AO erosion yield ($E_y$) of polymers using a curated dataset of 201 measurements compiled from multiple NASA LEO exposure missions. The model achieves an order of magnitude error (OME) of 0.154 and a log-scaled coefficient of determination (log-$R^2$) of 0.719, outperforming conventional machine learning approaches while eliminating the need for extensive feature engineering. Notably, incorporating macroscopic descriptors such as film thickness and layering did not improve predictive performance, suggesting that AO erosion is governed primarily by chemical and environmental variables rather than bulk structural characteristics. This finding underscores the importance of chemically informed polymer design in developing materials with improved AO resistance, while demonstrating the ability of modern LLMs to effectively extract chemically meaningful relationships even when data is limited. Beyond its predictive accuracy, the proposed framework provides a scalable platform for rapidly screening candidate polymers, reducing reliance on costly spaceflight experiments.