Beyond Beryllium: AI-Accelerated Materials Discovery for Interstellar Spacecraft Shielding
Yue Li, Xu Pan, Kaiyuan Guo

TL;DR
This paper uses AI and computational methods to identify new materials for spacecraft shielding, proposing a layered heterostructure that significantly reduces mass compared to traditional beryllium shields.
Contribution
It introduces a systematic AI-driven screening process for advanced shielding materials, combining DFT data and GNN predictions for interstellar spacecraft applications.
Findings
Hexagonal boron nitride and boron carbide identified as dual-function shielding materials.
Proposed layered heterostructure design reduces shield mass by 47%.
Screening methodology integrates DFT data with machine learning for material discovery.
Abstract
Project Daedalus (1973--1978), the most detailed interstellar probe design study ever conducted, specified a 9 mm beryllium erosion shield to protect the spacecraft payload during its 5.9 light-year cruise to Barnard's Star at 12% of the speed of light. This design, however, predated both the isolation of two-dimensional materials and the development of graph neural network (GNN) property predictors. Here, we systematically screen 20 candidate materials--spanning conventional aerospace metals, transition metal dichalcogenides, and ultra-high-temperature ceramics--using density functional theory (DFT) data from the JARVIS database (76,000 materials) with independent validation by the Atomistic Line Graph Neural Network (ALIGNN). We evaluate candidates across four criteria: specific mechanical stiffness (KV/rho), sputtering resistance, thermal neutron absorption cross-section, and…
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