Abstract

Quantum chemistry simulations are essential for connecting electronic behavior to macroscopic phenomena of materials, such as catalysis, spectroscopy, and electromagnetic responses. However, balancing computational cost and accuracy remains challenging due to the inherently complex nature of electronic structure. In this talk, I will discuss how this complexity hinders the development of universal, scalable solutions and propose guidelines for designing efficient quantum chemistry methods informed by chemical intuition. I will illustrate these guidelines through two computational algorithms I developed. First, I will introduce a new multi-reference approach that connects strong and weak electron correlations via a non-orthogonal configuration interaction (NOCI) formulation, showing that an efficient treatment of different types of electron correlation improves accuracy. Next, I will present density matrix embedding theory (DMET) and its finite-temperature formulation, demonstrating how understanding the entanglement structure enables a balance between accuracy and computational efficiency. Moving beyond chemical intuition, I will discuss the potential for a unified quantum chemistry framework. Our recent development of a neural network quantum state (NNQS) with a generative model based on normalizing flows paves the way for universal solutions in quantum chemistry.

Bio

Chong Sun is a postdoctoral scholar at Rice University with Prof. Gustavo Scuseria and a research scientist at Microsoft Quantum. Her research combines quantum chemistry, artificial intelligence (AI), and quantum information to advance the capabilities of materials simulations and discovery capabilities. Chong received her B.A. from Peking University, where she studied spin-crossover materials using density functional theory (DFT) and Monte Carlo simulations. She completed her Ph.D. at Caltech under Prof. Garnet Chan, where she developed classical and quantum algorithms for strongly correlated electrons. After her Ph.D., she was a postdoctoral fellow with Prof. Alán Aspuru-Guzik at the University of Toronto, developing machine-learning-inspired quantum chemistry methods.