MRI: Development of a Machine Learning Multimodal Ultrafast Optical Microscope

MRI：机器学习多模态超快光学显微镜的开发

• 批准号: 2117616
• 负责人: Libai Huang
• 金额: $ 76.69万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2117616&HistoricalAwards=false
• 关键词: MRI; Development; Machine; Learning; Multimodal

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.

该奖项由主要的研究工具，化学测量和成像以及化学研究工具计划共同支持。普渡大学正在开发机器学习多模式超快非线性光学显微镜，以支持Libai Huang教授和同事Gregery Buzzard，Sujith Puthiyaveetil，Michael Reppert和Chi Zhang的研究。通常，该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。如果成功的话，所产生的仪器将代表一种有能力的工具，该工具可以促进涉及复杂材料和生物系统的调查，这些材料和生物系统在广泛的时间内（10个飞秒 - 微秒 - 微秒）和长度（50 nm -micron）尺度，将功能扩展到当前使用常规商业微观仪器时可用的功能。 该时间/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。该工具将在多个部门的各个级别的学生的教育，研究和教学工作，并可以在其他机构使用。开发机器学习多模式超快光学成像平台的奖项旨在增强各个级别的研究和教育，尤其是在光学显微镜，机器学习和超快光谱等领域，通过将光学曝光和测量时间减少约100倍，而无需大量损失。将关注纳米材料中的连贯和非平衡能传输，光合膜中光反应的多尺度跟踪以及生物组装中的热流量以及专注于对机器学习支持的自适应采样的研究，以实现超级镜测量的超快测量。该仪器开发项目有望在时间和长度尺度上开放对复杂材料或生物系统的光学成像研究，超出了常规商业光学显微镜仪器的目前可用的，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。