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

该博士项目的目标是为扫描透射电子显微镜（STEM）开发一个完整的数字孪生模型，以实现实验、重建算法和自适应子采样方法的硅优化，从而在仪器功能的长度和时间尺度（微米到原子和皮秒到小时）上产生独特的科学见解。这一发展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，这将是显微镜中单个实验产生的许多相关图像/光谱。根据这一发展，预计图像优化，如聚焦、倾斜、污名和像差可以在采集过程中自我补偿，迈出了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，其中已经开发了原子分辨率图像/光谱的实时采集模式，其中通过关键的深度学习步骤辅助绘制重建-显微镜正在学习如何为自己拍摄最佳图像，然后优化实验采集。SenseAI目前正与几家主要仪器制造商合作，在现有的电子显微镜产品组合之外，将这些新方法扩展到使用x射线、离子、中子和光学仪器的仪器，目标是在不久的将来开发自动驾驶采集和分析能力。

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.