报告人简介：
Ji Chen, associate professor at School of Physics, Peking University. He obtained his doctorate degree from Peking University in 2014. He was a postdoctoral researcher at University College London, a Humboldt Postdoctoral Fellow at the Max Planck Institute for Solid State Research, and became an assistant professor at Peking University in 2018. He is mainly engaged in theoretical and computational research in condensed matter physics. His main research interests include first-principles and correlated electronic structure calculations, quantum Monte Carlo algorithms, and the development and application of machine learning and artificial intelligence algorithms in condensed matter physics, chemistry, and materials science. He has published more than 70 papers in top international academic journals. He takes charge of the Distinguished Young Scientists Project of the Beijing Natural Science Foundation in 2022.

讲座摘要：
In this talk, I will discuss a series of advancements in the development of the deep learning quantum Monte Carlo method, a new approach for calculating the many-body correlated electronic structures of molecules, materials and model systems from first principles. These advancements include improving accuracy and efficiency through the design of diffusion Monte Carlo and other neural network architectures, enhancing the calculation of molecular excited states, promoting and applying the method to the calculation of correlated electronic structures in solid materials, as well as exploring new applications in the calculation of the fractional quantum Hall effect and correlated topological states of matter.

邀请人：孙兆茹