Please use this zoom link for the event.
Please join us for a UW Data Science Seminar event on Wednesday, May 4th from 4:30 to 5:30 p.m. The seminar will feature Ting Cao, Assistant Professor of Materials Science at the University of Washington.
The event will be held in the UW Physics and Astronomy Auditorium (PAA 118)
“Machine Learning Quantum Materials”
Abstract: Over the past century, the development of quantum theory and scientific computing has enabled first-principles calculations of material properties with high accuracy. However, the computational cost of these first-principles methods, such as the density-functional theory, grows quickly with the system size, making the methods difficult to handle realistic quantum systems of current interest. In this talk, I will present how we overcome this difficulty by combining first-principles calculations with data science and machine learning, which result in new understandings of the quantum properties of materials. In the first part, we demonstrate that neural networks can be used to capture the force acting on ions in a superionic conductor. This leads to efficient simulation of ion dynamics with high accuracy. In the second part, we show a data science method to process spectroscopic images from measuring two-dimensional materials. The method successfully identifies a new form of quantum matter called “moiré magnets”. We further explore potential applications of our work in future clean energy and quantum information technology.
Biography: Ting Cao is an assistant professor of Materials Science & Engineering at the University of Washington. His research uses quantum physics, advanced modelling techniques, and high-performance parallel computing to understand condensed matter and predict material properties. He was awarded a GLAM fellowship by Stanford University in 2018 and named as an emerging leaders by “Journal of Physics: Condensed Matter” in 2021.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
The 2021-2022 seminars will be both in-person and virtual, and are free and open to the public.