Usami Noritaka, Kutsukake Kentaro, Kojima Takuto, Kudo Hiroaki, Yokoi Tatsuya, Ohno Yutaka
Graduate School of Engineering, Nagoya University, Nagoya, Japan.
Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya, Japan.
Sci Technol Adv Mater. 2024 Sep 18;25(1):2396272. doi: 10.1080/14686996.2024.2396272. eCollection 2024.
Multicrystalline materials play a crucial role in our society. However, their microstructure is complicated, and there is no universal approach to achieving high performance. Therefore, a methodology is necessary to answer the fundamental question of how we should design and create microstructures. 'Multicrystalline informatics' is an innovative approach that combines experimental, theoretical, computational, and data sciences. This approach helps us understand complex phenomena in multicrystalline materials and improve their performance. The paper covers various original research bases of multicrystalline informatics, such as the three-dimensional visualization of crystal defects in multicrystalline materials, the machine learning model for predicting crystal orientation distribution, network analysis of multicrystalline structures, computational methods using artificial neural network interatomic potentials, and so on. The integration of these research bases proves to be useful in understanding unexplained phenomena in complex multicrystalline materials. The paper also presents examples of efficient optimization of the growth process of high-quality materials with the aid of informatics, as well as prospects for extending the methodology to other materials.
多晶材料在我们的社会中发挥着至关重要的作用。然而，它们的微观结构很复杂，而且没有实现高性能的通用方法。因此，需要一种方法来回答我们应该如何设计和创建微观结构这个基本问题。“多晶信息学”是一种将实验科学、理论科学、计算科学和数据科学相结合的创新方法。这种方法有助于我们理解多晶材料中的复杂现象并提高其性能。本文涵盖了多晶信息学的各种原创研究基础，例如多晶材料中晶体缺陷的三维可视化、预测晶体取向分布的机器学习模型、多晶结构的网络分析、使用人工神经网络原子间势的计算方法等等。这些研究基础的整合被证明有助于理解复杂多晶材料中无法解释的现象。本文还展示了借助信息学对高质量材料生长过程进行有效优化的实例，以及将该方法扩展到其他材料的前景。
