Скачать или смотреть Designing Better Glass with Multi-Objective Bayesian Optimization with Paul Leu: SigOpt Summit 2021

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.

This talk was presented as a part of the 2021 SigOpt Summit.

✔️ Learn more about SigOpt: https://sigopt.com
✔️ Learn more about SigOpt Summit: https://summit.sigopt.com