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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. 

Since the surge of data in materials-science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular-dynamics engines such as LAMMPS and HOOMD-blue.