Studying complex microstructures with advanced transmission electron microscopy and machine learning

该项目旨在通过将新的无监督机器学习方法（特别是先进的聚类方法，如HDBSCAN和概率模糊聚类）应用于相关的结构和组成数据，开发新的微观结构表征。UoM拥有独特的透射电子显微镜，能够同时记录高质量的衍射和EDX测量，学生将优化此数据采集所需的实验参数。他们还将研究与实验测量一起工作所需的数据科学。特别是以前的研究已经确定预处理，数据合并和流形变换方法的多维数据的基本步骤，使其适合机器学习。这将使微结构的细微之处能够比以前更可靠地识别。这些方法将作为正在进行的新型锂电池正极材料、工程合金和功能陶瓷的结构-成分-性能分析研究的一部分。

This project aims to develop novel microstructure characterization through the application of novel unsupervised machine learning methods (in particular advanced clustering approaches such as HDBSCAN and probabilistic fuzzy clustering) to correlated structure and composition data. The UoM has a unique transmission electron microscope capable of recording simultaneous high quality diffraction and EDX measurements and the student will optimize the experimental parameters needed for this data acquisition. They will also investigate the data science needed to work with the experimental measurements. Particularly previous studies have identified preprocessing, data merging and manifold transformation method of the multi-dimensional data as essential steps to make it amenable to machine learning. This will allow subtleties of microstructure to be identified with greater reliability than was previously possible. The methods will be developed as part of ongoing research into the structure-composition-property analyses of novel lithium battery cathode materials, engineering alloys and functional ceramics.