MRI: Development of a Machine Learning Multimodal Ultrafast Optical Microscope

该奖项由主要研究仪器，化学测量和成像以及化学研究仪器计划共同支持。普渡大学正在开发一种机器学习多模态超快非线性光学显微镜，以支持黄立白教授及其同事Gregery Buzzard，Sujith Puthiyaveetil，Michael Reppert和Chi Zhang的研究。总的来说，这种仪器开发结合了仪器设计，非线性超快光谱，显微镜和机器学习方面的专业知识。如果成功，所产生的仪器将代表一种使能工具，可以促进在广泛的时间（10飞秒-微秒）和长度（50纳米-微米）尺度上涉及复杂材料和生物系统的调查，扩展功能超出目前传统的商业显微镜仪器。 这种时间/空间信息可用于更好地理解复杂材料和生物样品中的能量和热流。这一工具将加强教育，研究，并在各级学生的教学努力，在几个部门，以及可供使用的其他机构。该奖项旨在开发机器学习多模态超快光学成像平台，旨在加强各级研究和教育，特别是在光学显微镜，机器学习和超快光谱学等领域，通过将光学曝光和测量时间减少约100倍，而不会显着降低重建图像质量。研究重点是纳米材料中的相干和非平衡能量传输，光合膜中光响应的多尺度跟踪，以及生物组件中的热流，以及那些专注于机器学习的自适应采样的超快显微镜测量。该仪器开发项目有望在时间和长度尺度上对复杂材料或生物系统进行光学成像研究，这超出了目前常规商业光学显微镜仪器的范围。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is jointly supported by the Major Research Instrumentation, the Chemical Measurement and Imaging, and the Chemistry Research Instrumentation programs. Purdue University is developing a machine learning multimodal ultrafast nonlinear optical microscope to support the research of Professor Libai Huang and colleagues Gregery Buzzard, Sujith Puthiyaveetil, Michael Reppert, and Chi Zhang. In general, this instrument development combines expertise in instrument design, non-linear ultrafast spectroscopy, microscopy, and machine learning. If successful, the resulting instrument will represent an enabling tool that could facilitate investigations involving complex materials and biological systems over a wide range of time (10 femtoseconds - microseconds) and length (50 nm - micron) scales, extending capabilities beyond what is currently available with conventional commercial microscopy instruments. This temporal/spatial information may be used to better understand energy and heat flow in complex materials and biological samples. This instrument will enhance education, research, and teaching efforts of students at all levels, in several departments, as well as be accessible for use at other institutions. The award to develop a machine learning multi-modal ultrafast optical imaging platform is aimed at enhancing research and education at all levels, especially in areas such as optical microscopy, machine learning, and ultrafast spectroscopy by reducing optical exposure and measurement time by about 100-fold without significant loss in reconstructed image quality. Studies focused on coherent and non-equilibrium energy transport in nanomaterials, multi-scale tracking of light response in photosynthetic membranes, and heat flow in biological assemblies are to be pursued as are those focused on machine learning-enabled adaptive sampling for ultrafast microscopy measurements. This instrument development project has the promise of opening up optical imaging studies of complex materials or biological systems at time and length scales beyond what is currently available with conventional commercial optical microscopy instrumentation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.