Electronic Structure Reading Group: Gaussian Process Regression for Materials and Molecules
Event details
| Date | 03.02.2025 |
| Hour | 16:00 › 17:30 |
| Speaker | Anna Paulish |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
In this talk, I will introduce the fundamentals of Gaussian process regression, covering key concepts. I will discuss sparse GPR techniques for efficiently handling large datasets, such as the Nyström method and subset of regressors, as well as its application to materials modeling problems.
References:
- Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. The MIT Press.
- Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Csányi, G. (2021). Gaussian Process Regression for Materials and Molecules. Chemical Reviews, 121(16), 10073–10141. https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.1c00022
The electronic structure reading group brings together researchers and students interested in mathematical aspects of electronic structure problems and adjacent topics, including:
- Density Functional Theory
- Many-body Schrödinger equation for electrons
- Born-Oppenheimer Molecular Dynamics
- Numerical analysis and error control
Website: https://matmat.org/readinggroup/
Practical information
- General public
- Free
Organizer
Contact
- Michael Herbst (EPFL), Niklas Schmitz (EPFL)