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SigOpt Summit 2021
November 16, 2021, Online
SigOpt Summit 2021
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SigOpt Summit 2021
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Better Glass Design with Multi-Objective Bayesian Optimization
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About the talk

Paul Leu, Associate Professor in the Laboratory for Advanced Materials (LAMP) at the University of Pittsburgh, discusses his experience collaborating with the SigOpt team to accelerate the development of new fabrication strategies for glass to improve its performance in key properties, such as reducing haze or reflectance.

Many modern consumer electronic devices such as smartphones and tablets require the use of specialized glass or plastic materials to protect the device’s delicate display, minimize haze, and resist substances like dirt, water, and grease. Historically, nanostructured surface research is slow and fragmented due to the use of trial-and-error design methods. Nanostructured surface experiments require the precise selection of various fabrication parameters, such as the flow rate of various gases, ion etching time, chamber pressure, and more. Numerical simulations exist, but can be slow and inaccurate in the most useful circumstances. Moreover, the fabrication process is time consuming: one fabrication in one of our experimental settings requires 16 hours of chemical vapor deposition.

How can we efficiently search for the desired fabrication parameters and, in the process, speed up nanostructured surface research? The answer is multiobjective Bayesian optimization. Bio-inspiration and advances in micro-/nanomanufacturing processes have enabled the design and fabrication of micro-/nanostructures to create a variety of functionalities. In this talk, Paul discusses his research group’s recent progress in the creation of multi-functional glass using multi-objective Bayesian optimization.

About speaker

Paul Leu
Associate Professor at University of Pittsburgh

Prof. Paul Leu is an Associate Professor and the BP America Faculty Fellow in the Industrial Engineering Department at the University of Pittsburgh. His research group the Laboratory for Advanced Materials at Pittsburgh (LAMP) focuses on functional materials which have included functionalities such as antireflection, light trapping, and haze control in plasmonics, transparent electrodes, and solar cells. Recently, he has been integrating simulation and experimental methodologies with machine learning for materials discovery. He has been recipient of the Oak Ridge Associated University Powe Junior Faculty Enhancement Award, UPS Minority Advancement Award, and the NSF CAREER Award. His research has been showcased in Scientific American Frontiers, Pittsburgh NPR, and Pittsburgh Magazine.

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