06.02.2018 - Rodrigo Vargas-Hernández - Machine learning for many-body quantum physics

Solving the Schrödinger equation (SE) for many-body systems has been the main challenge for quantum physicists over the last century. In this talk, I will illustrate how with machine learning it may be possible to reduce the number of times the SE needs to obtain physical results. In the first part of my talk I will explain the application of kernel methods of machine learning for fitting potential energy surfaces (PES) of polyatomic molecules. In particular, I will illustrate the application of Bayesian optimization with Gaussian processes as an efficient method for sampling the configuration space of polyatomic molecules. In the second part of my talk I will discuss the possibility to extrapolate quantum observables with machine learning to discover phase transitions for condensed matter systems.

See poster.