/ 首页 / 学术交流 / 正文

报告题目:Modeling Shape Transformations in Liquid Crystal Elastomers: A Machine Learning Approach to Inverse Design

报告时间:2023 年 11 月 2 日 (周四) 下午16:00

报告地点:3号楼307会议室

报告人:Prof. Robin Selinger

邀请人:叶方富  研究员

 

Biography: Robin Selinger completed her AB, AM, & PhD studies in physics at Harvard University. After postdoctoral work at UCLA, University of Maryland, & NIST, she joined the physics faculty at the Catholic University of America in 1995. In 2005 she joined the faculty at Kent State University where she is affiliated with both the Physics Dept. & the  Advanced Materials & Liquid Crystal Institute. In recent years she has served as a member of the American Physical Society (APS) Board of Directors & Council of Representatives, &in 2022 served as Speaker of the Council. In outreach activities, she organizes STEM internships for high school students enrolled at Kent State via College Credit Plus. Her research interests include theoretical/computational studies of soft matter.
Abstract: Liquid Crystal Elastomers (LCE) are stimuli-responsive, programmable actuators that undergo shape-morphing in response to a change of temperature, illumination, or other environmental cues. The resulting actuation trajectory is programmed by patterning the nematic director field, e.g. by forming the sample between glass substrates with prescribed surface anchoring patterns which may be identical or entirely different. Using a GPU-based finite element simulation developed in-house, we explore mechanisms by which arrays of topological defects in the microstructure of LCE thin coatings give rise to transformations in surface topography. We also develop a machine learning algorithm to optimize the shape of resulting topological features.

上一篇: Cancer Nanomedicine: From Fundamental Nano-bio Interactions to Targeted Delivery
下一篇: Director Deformations, Geometric Frustration, and Modulated Phases in Liquid Crystals