In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystalline materials (i.e. grains, grain boundaries [GBs], and microstructures) and summarize their various aspects (experimental synthesis, artificial single GBs, multiscale experimental data acquisition via electron microscopy, formation process modeling, property description modeling, 3D reconstruction, and data-driven design methods). Specifically, we discuss a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using BOXVIA software. As a proof of concept, a researcher- and data-driven process design methodology is applied to a polycrystalline iron-based superconductor to evaluate its bulk magnet properties. Finally, future challenges and prospects for data-driven material development and iron-based superconductors are discussed.
Keywords: 3D reconstruction; 4D STEM; BOXVIA; Bayesian optimization; DFT; Iron-based superconductor; data assimilation; data-driven process design; deep learning; electron microscopy; fully convolutional neural networks; grain boundaries; high-energy milling process; machine learning; magnet; multiscale observation; phase-field modeling; polycrystalline materials; processing; researcher-driven process design; scanning precession electron diffraction; superconductor; thin films; trapped field.
We present a new set of machine learning-based materials research methodologies for polycrystalline materials, such as a mechanochemical process involving high-energy milling, in situ observation of microstructural formation using 3D scanning transmission electron microscopy, phase-field modeling coupled with Bayesian data assimilation, nano-orientation analysis via scanning precession electron diffraction, semantic segmentation using neural network models, and the Bayesian-optimization-based process design using the BOXVIA software.
© 2025 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.