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)