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This dataset accompanies the manuscript by H. Du, H. Pan, and J. Dshemuchadse,“Pressure-Driven Solid–Solid Phase Transformations of Isotropic Particles Across Diverse Crystal Structure Types”, in publication (2025). In this work, we investigated the influence of pressure on the behavior of 16 crystal structure types that have been shown to self-assemble in molecular dynamics simulations using isotropic, pairwise interaction potentials. We studied these diverse structures using a range of computational models as a function of pressure, characterized the high-pressure phases, identified four previously unknown crystal structure types, and categorized the observed phase transformations. This dataset includes the representative simulation trajectories (in .gsd file format) mentioned in the main text and the Supplemental Material. A README.txt file is included to assist with parsing the data. We hope that this dataset will be useful for future research on pressure-induced phase transformations in both experimental and simulation studies. 

This dataset accompanies the “Local structural features elucidate crystallization of complex structures” preprint (https://arxiv.org/abs/2401.13765) by M. M. Martirossyan, M. Spellings, H. Pan, and J. Dshemuchadse. This dataset is built to be used in conjunction with the GitHub code (https://github.com/capecrystal/local-structural-features) for training order metrics with machine learning methods. In this work, we show that this method can distinguish different crystallographic sites in highly complex structures of varying complexity and coordination number, and it can be used to study the growth trajectories of such structures. The dataset includes self-assembly trajectories from 10 different crystal structures and 2 trajectories of the same structure assembling via different crystallization pathways. A README.txt file is included for parsing the data.