Caltech Postdocs Launch – October 25, 2024
- Public Event
Lunch will be served at 11:45 AM
Dr. Sejun Kim
New deep learning model for materials with a commentary on Nobel Prize
In this talk, I will introduce a new deep learning model for predicting material properties and offer a brief commentary on the recent Nobel Prize in Physics and Chemistry. This year's award highlights significant advancements in machine learning that have transformed scientific discovery. The laureates developed innovative algorithms that leverage vast datasets, providing unprecedented insights across various fields. By examining these advancements, I will demonstrate how my model addresses the growing need for accurate predictions grounded in basic chemical concepts and outline future research directions for real-world applications.
Dr. Wenjie Zhou
Polycatenated Architected Materials
Inspired by the intricate entanglement seen in polymer microstructures, we introduce polycatenated architected materials (PAMs), a novel class of materials composed of interlocking rings or caged particles organized into 3D networks. PAMs are remarkable for their ability to switch between solid-like and fluid-like behavior under different load conditions. Additionally, they respond dynamically to electrical charges, enabling remotely controlled shape morphing. From a fundamental standpoint, PAMs bridge principles of solid and granular mechanics while introducing new physics; and their tunable mechanical properties offer vast potential for innovative applications in robotics, protective gear, and adaptive materials, pushing the boundaries of material design.