MicrographBank: Interfacial Intermetallics

A high-throughput synthetic dataset of heterogeneous microstructure morphologies generated via multi-phase-field simulations and machine learning analysis Description: This Interfacial Microstructure Atlas is a curated dataset consisting of 1672x25 or 41800 microstructure images derived from multi-phase-field simulations designed to explore the effects of uncertain interfacial energies on intermetallic compound (IMC) morphologies. Developed within the framework of Integrated Computational Materials Engineering (ICME), this dataset systematically samples the 3D interface energy space (σSI, σSL, σIL) using Sobol sequences and captures the transient evolution of the Cu6Sn5 phase during heterogeneous solidification in Cu/Sn systems. Each image encodes the phase-field order parameter of the IMC and has been analyzed using Fourier Descriptors, semi-supervised learning (label spreading), and variational autoencoders (VAE) to classify the shapes into wetting, invariant, and dewetting regimes. The dataset enables studies on: Microstructure classification Uncertainty propagation in PDE-based models Process-Structure-Property (PSP) linkages Deep learning applications in synthetic materials data Key Features: 1,672 phase-field simulations with distinct interfacial energy parameters, 1672 x 25 or 41800 microstructure images 388×343 px² raw images, preprocessed to 64×64 px² Corresponding Fourier Descriptors and interface energy labels Label spreading–based shape classification and VAE latent embeddings The data was interpreted in the following article: Machine Learning-Assisted High-Throughput Exploration of Interface Energy Space in Multi-Phase-FieldModel with CALPHAD potential Vahid Attari, Raymundo Arroyave Texas A&M University Link to article: https://doi.org/10.1186/s41313-021-00038-0 For more information please refer to Open Phase-field Microstructure Database (OPMD) curated at https://microstructures.net.