This dataset accompanies the manuscript by H. Pan and J. Dshemuchadse, “Targeted Discovery of Low-Coordinated Crystal Structures via Tunable Particle Interactions” ACS Nano 17(8), 7157–7169 (2023). In this work, we designed a new isotropic pair potential, in which all energy wells and maxima can be tuned independently. We use our interaction potential design to systematically explore self-assembled crystal structure configurations, specifically targeting low-coordinated structures. We report the computational self-assembly of 20 new crystal structure types, 14 of which are low-coordinated, and we investigate the relationship between features of the interaction potential and the resulting structures. This dataset includes over 2000 final simulation frames of the resulting crystal droplets (.gsd) and the corresponding interaction potential and simulation parameters (.json) used in this study. A README.txt file is included for parsing the data. We hope that this dataset will be useful for a variety of future work, both fundamental in nature as well as data-centric applications related to self-assembly, interaction potentials, and complex crystal structures.