tokens: of neutron-rich nuclei are required in r -process simulations, and most of these nuclei lie far beyond the experimentally known region. Theoretical predictions of the properties of these neutron-rich nuclei have large uncertainties, which can further interplay with the astrophysical uncertainties. This makes it even more difficult to address the issues of the r -process. Nuclear masses are extremely important for r -process simulations, as they are needed to extract the reaction energies that go into the calculations of all involved nuclear reaction rates. During recent decades, great achievements in nuclear mass measurements have been made thanks to the development of radioactive ion beam facilities, and about 2500 nuclear masses have been measured to date (Wang et al. 2012 , 2017 , 2021 ). Nevertheless, the masses of a large number of neutron-rich nuclei relevant to the r -process remain unmeasured and cannot be reached in the foreseeable future, due to the difficulties in production, separation, and detection. Therefore, reliable theoretical predictions for nuclear masses are imperative at present. However, an accurate prediction of nuclear masses is still a great challenge for nuclear theory due to the difficulties in understanding nuclear interactions and quantum many-body systems. Tremendous efforts have been made in improving the prediction of nuclear masses and, currently, the rms deviations between theoretical mass models (Pearson et al. 1996 ; Koura et al. 2005 ; Zuker 2008 ; Goriely et al. 2009 ; Wang et al. 2014 ; Möller et al. 2016 ) and the available experimental data reach about 0.5 MeV. The rms deviations are further reduced to about 0.2 MeV by employing machine learning approaches (e.g., Wang Liu 2011 ; Niu et al. 2013 ; Utama et al. 2016 ; Neufcourt et al. 2018 ; Niu Liang 2018 ; Wu Zhao 2020 ). However, different mass models predict very different masses when moving to the region of neutron-rich nuclei and, thus, lead to obviously different abundances of nuclei in the r -process nucleosynthesis simulations (e.g., Wanajo et al. 2004 ; Sun et al. 2008 ; Farouqi et al. 2010 ; Arcones Martínez-Pinedo 2011 ; Martin et al. 2016 ; Li et al. 2019 ; Zhao Zhang 2019 ). Therefore, it is important to address how the nuclear mass uncertainties limit our knowledge of the r -process (e.g., Sun et al. 2008 ; Farouqi et al. 2010 ; Arcones Martínez-Pinedo 2011 ; Brett et al. 2012 ; Xu et al. 2013 ; Aprahamian et al. 2014 ; Martin et al. 2016 ; Mendoza-Temis et al. 2016 ; Mumpower et al. 2016 ; Li et al. 2019 ; Sprouse et al. 2020b ). Addressing the impact of mass uncertainties on r -process abundances is useful to deepen our understanding of the r -process in the present stage. For instance, can we identify the dominant r -process site with current uncertainty (e.g., Mumpower et al. 2015c ; Martin et al. 2016 )? Can we understand the origin of the rare-earth peak (e.g., Surman et al. 1997 ; Goriely et al. 2013 )? To what extent can we
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