The development of open source python package PySCF and relevant quantum chemistry methodologies
Python package PySCF was initiated to supporting quantum embedding theory. During the development of PySCF package, this project was directly or indirectly involved into various novel quantum chemistry methodologies, including but not limited to density matrix embeddings, matrix product states and tensor network states, quantum Monte Carlo, selected Configuration interactions, quantum neural network states, deep neural network for chemistry and Shrodinger equations. Unlike the traditional first principle methodologies which have particularly strict mathematical frameworks and deterministic computational algorithms, these newly proposed quantum chemistry models stands at the other end that utilizes in the model more physical insights or stochastic characters of the many body systems. Along with the methodology development, new computing and programming technologies were also employed or developed. Single-side MPI (message passing interface) implementation, distributed memory tensor contraction engine CTF were used.
孙启明博士毕业于北京大学化学系理论与计算化学专业，在普林斯顿大学进行了博士后训练，此后在加州理工学院任研究员，在腾讯量子实验室任首席科学家，现在安贤基金管理公司主管技术开发。主要工作包括量子化学程序包 PySCF 的开发，积分程序库 libcint 的开发，相对论量子化学算法，核磁共振量子化学算法，quantum embedding 方法，周期性体系积分问题，周期性体系 Coupled Cluster 方法，大体系 MCSCF 算法，quantum neural network states 等量子化学理论算法的发展。
邀请人：刘 淼 副研究员（9407）