Neural canonical transformation leverages modern generative models to parametrize variational density matrices of many-particle systems and optimizes them via variational free energy minimization. In this talk, I will first introduce physical motivations of the approach with examples. Then, I will present some recent results related to phase transitions in lithium solid and water ice.
Lei Wang is a Professor at Institute of Physics, Chinese Academy of Science. He got his Bachelor’s degree from Nanjing University in 2006 and Ph.D. from the Institute of Physics, Chinese Academy of Sciences in 2011. He did postdoctoral research on computational quantum physics at ETH Zurich in the next few years. Lei Wang joined the Institute of Physics in 2016. His research interest is at the cross-section of machine learning and quantum many-body computation.