| Atomic masses with machine learning for the astrophysical r process M Li, TM Sprouse, BS Meyer, MR Mumpower Physics Letters B 848, 138385, 2024 | 10 | 2024 |
| Bayesian averaging for ground state masses of atomic nuclei in a machine learning approach M Mumpower, M Li, TM Sprouse, BS Meyer, AE Lovell, AT Mohan Frontiers in Physics 11, 1198572, 2023 | 9 | 2023 |
| Dependence of equilibrium -process abundances on nuclear physics properties M Li, BS Meyer Physical Review C 106 (3), 035803, 2022 | 3 | 2022 |
| Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling WS Porter, B Liu, D Ray, AA Valverde, M Li, MR Mumpower, M Brodeur, ... Physical Review C 110 (3), 034321, 2024 | 1 | 2024 |
| Atomic Masses with Machine Learning for the Astrophysical R-process M Li, TM Sprouse, B Meyer, MR Mumpower APS Division of Nuclear Physics and the Physical Society of Japan, 2023 | | 2023 |
| GrRproc: A graph-based method to calculate r-process abundances M Li, B Meyer Joint RIKEN/N3AS Workshop on Multi-Messenger Astrophysics, https://n3as …, 2023 | | 2023 |
| Extrapolating Mixture Density Network predictions: application to the astrophysical r-process M Li, M Mumpower, T Sprouse, A Lovell, A Mohan, B Meyer Bulletin of the American Physical Society 67, 2022 | | 2022 |
| Dependence of (n, γ) - (γ, n) Equilibrium r-Process Abundances on Nuclear Physics Properties M Li, B Meyer APS Division of Nuclear Physics 66, 2021 | | 2021 |
