Machine Learning-Directed Discovery of Ultrahigh Young’s Modulus Crystals

Materials with ultrahigh Young’s modulus are essential for advanced applications such as aerospace components and energy devices. Efficiently identifying the maximum Young’s modulus of materials is a key factor in accelerating the development of advanced high-stiffness materials. In this study, we developed a model based on a crystal graph convolutional neural network, incorporating data processing and model optimization techniques. These approaches effectively address data scarcity and imbalance in high-modulus crystals while significantly enhancing prediction accuracy, reducing the mean absolute error to 30.6 GPa on the test set. Guided by the model and validated through first-principles calculations, we conducted high-throughput screening on a comprehensive data set of over 1.16 million crystals. As a result, we identified 31 ultrahigh Young’s modulus crystals, all exceeding 1000 GPa. Among them, OsO exhibited an exceptional modulus of 1557.9 GPa, making it one of the highest Young’s modulus crystals discovered through machine learning-guided screening. Furthermore, most of these crystals have formation energies below 0.4 eV/atom, indicating favorable thermodynamic stability and potential experimental synthesizability, making them promising candidates for advanced structural applications. This study presents an effective approach for the discovery of ultrahigh Young’s modulus crystals. The proposed method significantly enhances the accuracy and efficiency of maximum Young’s modulus identification, providing a valuable strategy for accelerating the development of next-generation advanced materials for structural and engineering applications.