M achine Learning-Driven Elasticity Prediction in Advanced Inorganic Materials via Convolutional Neural Networks

The final predicted dataset consists of two parts: one part from the Materials Project database, which contains 54,359 crystal structures (referred to as the Materials

Project

Elastic Dataset, MPED); the other part consists of 26,305 crystal structures discovered by Merchant et al. (referred to as the Nature Elastic Dataset, NED). Together, these two datasets form the comprehensive dataset of elastic properties for 80,664 inorganic crystal structures, providing essential reference data for material design and performance prediction.The data file contains the following information for each material: the Crystallographic Information File (CIF), Formula, Number of Atoms, Density (g cm⁻³), Volume (Å), Total Atomic Mass (amu), Shear Modulus (GPa), Bulk Modulus (GPa), Sound Velocity of Longitudinal Wave (m s⁻¹), Sound Velocity of Transverse Wave (m s⁻¹), Speed of Sound (m s⁻¹), Poisson's Ratio, and Debye Temperature (K).