Constructing a Digital Twin for a self-correcting Scanning Transmission Electron Microscope using Machine Learning Approaches

使用机器学习方法构建自校正扫描透射电子显微镜的数字孪生

• 批准号: 2889721
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889721
• 关键词: Constructing; Digital; Twin; self; correcting

The goal of this PhD project is to develop a full digital twin model for the Scanning Transmission Electron Microscope (STEM) to enable in silico optimisation of experiments, reconstruction algorithms and the adaptive sub-sampling approach needed to generate unique scientific insights across the length and timescales of the instrument function (microns to atoms and picoseconds to hours). Included in this development will be statistical emulation using a multi-output Gaussian Process (MOGP), which enables machine learning algorithms to consider uncertainty across multiple signals, which in this case will be the many correlated images/spectra generated by a single experiment in the microscope. From this development it is anticipated that image optimisations such as focus, tilt, stigmation and aberrations can be self-compensated for during acquisition, taking the key first steps to self-driving experimental approaches to materials characterisation. The initial applications of this technology have focused on electron microscopy, where a real time acquisition mode for atomic resolution images/spectroscopy has been developed in which inpainting reconstructions are aided by a critical deep learning step - the microscopes are learning how to take the best images for themselves and then optimising the experimental acquisition. SenseAI is now working with several major instrument manufacturers to broaden these new approaches to instruments using X-rays, ions, neutrons and optics in addition to the existing portfolio of electron microscopes, with the goal of developing self-driving acquisition and analysis capabilities in the near future.

该博士项目的目标是为扫描透射电子显微镜(STEM)开发一个全数字孪生模型，以实现实验、重建算法和自适应亚采样方法的计算机优化，以产生对仪器功能的长度和时间尺度(微米到原子和皮秒到小时)的独特科学见解。这一开发中将包括使用多输出高斯过程(MOGP)的统计仿真，它使机器学习算法能够考虑多个信号的不确定性，在这种情况下，多个信号将是由显微镜中的单个实验生成的许多相关图像/光谱。从这一发展中预计，图像优化，如聚焦、倾斜、污点和像差，可以在采集过程中自我补偿，迈出材料表征的自动驾驶实验方法的关键第一步。这项技术的最初应用集中在电子显微镜上，已经开发出一种原子分辨率图像/光谱的实时采集模式，其中修复重建得到了关键的深度学习步骤的帮助-显微镜正在学习如何为自己获取最好的图像，然后优化实验采集。SenseAI目前正在与几家主要仪器制造商合作，将这些新方法扩大到使用X射线、离子、中子和光学的仪器，并在现有的电子显微镜产品组合之外，目标是在不久的将来开发自动驾驶采集和分析能力。