Mengke Li

Publications

Wed, 30 Sep 2026 00:00:00 +0000

Submitted #

Machine Learning β-decay Half-Life and Their Application to r-Process Observables
M. Li, M. Mumpower, N. Vassh, R. Surman. (submitted to The Astrophysical Journal, 2026)

Published #

Neutron Economy in Late Time r-process Nucleosynthesis
M. Li, B. Meyer.
The Astrophysical Journal (2026)
Implications of a Weakening N = 126 Shell Closure Away from Stability for r-Process Astrophysical Conditions
M. Li, G. C. McLaughlin, R. Surman.
Physics Letters B (2026)
Constraining Nuclear Mass Models Using r-Process Observables with Multi-objective Optimization
M. Li, M. R. Mumpower, N. Vassh, W. S. Porter, R. Surman.
Physical Review Research Letters (2025)
Graph-based Recursive Relations for Computing and Analyzing r-Process Abundances
M. Li, B. S. Meyer.
The Astrophysical Journal (2025)
Investigating the Effects of Precise Mass Measurements of Ru and Pd Isotopes on Machine Learning Mass Modeling
W. S. Porter, B. Liu, D. Ray, A. A. Valverde, M. Li, M. R. Mumpower, et al.
Physical Review C (2024)
Atomic Masses with Machine Learning for the Astrophysical r-Process
M. Li, T. M. Sprouse, B. S. Meyer, M. R. Mumpower.
Physics Letters B (2024)
Bayesian Averaging for Ground State Masses of Atomic Nuclei in a Machine Learning Approach
M. R. Mumpower, M. Li, T. M. Sprouse, B. S. Meyer, A. E. Lovell, A. Mohan.
Frontiers in Physics (2023)
Dependence of (n,γ)–(γ,n) Equilibrium r-Process Abundances on Nuclear Physics
M. Li, B. S. Meyer.
Physical Review C (2022)

Research

Wed, 30 Sep 2026 00:00:00 +0000

Research Overview #

My research is in theoretical nuclear astrophysics, with a focus on understanding the origins of heavy elements in the universe through nucleosynthesis processes occurring in extreme astrophysical environments such as neutron star mergers and core-collapse supernovae.

I combine nuclear theory, astrophysical simulations, and machine learning to connect properties of exotic nuclei with observable r-process signatures. My work uses nuclear mass models, beta-decay and reaction-rate calculations, reaction-network modeling, and data-driven methods to understand how nuclear uncertainties shape the elemental abundances observed in the Solar System and in metal-poor stars.

About

Mon, 01 Jan 2024 00:00:00 +0000

Welcome! I am an N3AS Postdoc Fellow working on theoretical nuclear astrophysics at UC Berkeley. My research sits at the intersection of astrophysics, nuclear physics, and machine learning, exploring how exotic nuclear properties shape cosmic phenomena like r-process nucleosynthesis and kilonova.

I received my PhD from Clemson University in December 2023, under the supervision of Prof. Bradley Meyer. As a Postdoc Fellow, I am co-mentored by Rebecca Surman (University of Notre Dame), Gail McLaughlin (NC State University) and Dan Kasen (UC Berkeley).

Mentorship

Mon, 01 Jan 2024 00:00:00 +0000

Mentoring #

2025 — Research mentor for UC Berkeley undergraduate student Flora Wang
2024 — Career mentor for UC Berkeley undergraduate student Claire T. Chen

Teaching Experience #

Graduate Teacher of Record (Course Leader) — Clemson University (2022–2023)
- Designed experimental laboratory modules and updated course curriculum.
- Managed digital course materials, assignments, and gradebooks via Canvas LMS.
- Coordinated grading criteria and course assessments to track learning outcomes.
Graduate Teaching Assistant — Clemson University (2018–2021)
- Instructed introductory and intermediate physics lab sections (PHYS 2070, 2090, 2100, 2300).
- Mentored students in experimental techniques, data reduction, and scientific writing.
- Graded lab reports and assessments while providing detailed, constructive feedback.