In statistical physics, Monte Carlo methods have been an important computational techniques to study the thermodynamics and properties of matter. They use random sampling to generate a collection of physical states of a system, from which finite- temperature properties of materials can be calculated. The statistical analysis associated with Monte Carlo simulations naturally extends to the use of machine learning methods that have been rapidly advancing in recent years. Additionally, these two methods complement each other to facilitate new algorithms, capabilities, as well as applications to frontier research problems. In this colloquium, I will review the basic principles and developments of both Monte Carlo and machine learning methods, and highlight some of our current studies in condensed matter physics enabled by the combination of these two methods.

Bio:
Ying Wai Li has a Ph.D. in Computational Physics. She is a UGA Physics and Astronomy alumni, and is currently Deputy Team Leader of the Future Architectures and Applications Team at Los Alamos National Laboratory.  Li’s research interests span condensed matter physics, algorithm design, and high-performance computing. Her expertise is in the state-of-the-art classical and parallel Monte Carlo algorithms, first principles methods, and recently the application of machine learning to computer simulations and data analytics for the study of thermodynamics and phase transitions of material properties.