The Physics & Astronomy Colloquium Series presents Jane Kim of Ohio University, discussing "Towards a New Era of Discovery in Low-Energy Nuclear Science" on April 11.

Abstract: Capturing entanglement and emergent phenomena in the many-body wave function of strongly correlated fermions is both computationally demanding and conceptually challenging, with broad applications in nuclear physics, quantum chemistry, condensed matter, and beyond. In recent years, neural quantum states (NQS) have emerged as powerful tools for representing wave functions, breathing new life into variational methods with modern machine learning techniques. 

In this talk, I will discuss how recent advancements in machine learning have been integrated into NQS architectures, enabling them to capture subtle correlations in fermionic systems such as electron gases, cold Fermi gases, nuclear matter, and nuclei. I will highlight how emergent structures and correlations arise from microscopic interactions and how NQS could be extended to improve our models of effective nuclear interactions.