MRI: Development of a Machine Learning Multimodal Ultrafast Optical Microscope

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.