Announcement Detail
Wednesday, September 25, 2024
2:00 PM CST
Join via Zoom: https://us06web.zoom.us/j/84541274013?pwd=ZDhuM09BaUlVVlBuREhEV0sweGFlZz0
Nanotechnology and Lower Scale Phenomena Webinar Series
The fall semester monthly seminar series features invited talks by experts from different fields such as mechanics, material science, mathematics, physics, and chemistry. The monthly seminar will take place via Zoom, preferably on the last Wednesday, and will be approximately 45 minute talk followed by a 10-15 minute discussion.
More than physics, more than data: Integrated machine-learning models for materials
Speaker
Prof. Michele Ceriotti, EPFL
Abstract
Machine-learning techniques are often applied to perform "end-to-end" predictions, that is to make a black-box estimate of a property of interest using only a coarse description of the corresponding inputs. In contrast, atomic-scale modeling of matter is most useful when it allows one to gather a mechanistic insight into the microscopic processes that underlie the behavior of molecules and materials. In this talk I will provide an overview of the progress that has been made combining these two philosophies, using data-driven techniques to build surrogate models of the quantum mechanical behavior of atoms, enabling "bottom-up" simulations that reveal the behavior of matter in realistic conditions with uncompromising accuracy. I will discuss two ways by which physical-chemical ideas can be integrated into a machine-learning framework. One way involves using physical priors, such as smoothness or symmetry of the structure-property relations, to inform the mathematical structure of a generic ML approximation. The other entails a deeper level of integration, in which explicit physics-based models and approximations are built into the model architecture. I will discuss several examples of the application of these ideas, from the calculation of electronic excitations to the design of solid-state electrolyte materials for batteries and high-entropy alloys for catalysis, emphasizing both the accuracy and the interpretability that can be achieved with a hybrid modeling approach, and providing an overview of the exciting research directions that are made available by these new modeling tools.
Biography
I received my Ph.D. in Physics from ETH Zürich, and spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 I have led the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, focusing on method development for atomistic materials modeling, bridging quantum mechanics, statistical mechanics and machine learning. If you're curious about my grants and prizes, Google is your friend. If you're curious about stuff I actually care about, I am very proud to have contributed to the development of several open-source software packages, including http://ipi-code.org and http://chemiscope.org, and to have served the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials.
Upcoming webinar speakers:
Wed. October 30: Prof. Taher Saif, University of Illinois Urbana-Champaign
Wed. November 27: Prof. Gábor Csányi, University of Cambridge