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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. 

Colloidal and nanoparticle self-assembly enables the creation of ordered structures with a variety of electronic and photonic functionalities. The outcomes of the self-assembly processes used to synthesize such structures, however, strongly depend on the uniformity of the individual nanoparticles. Here, we explore the simplest form of particle size dispersity—bidispersity—and its impact on the self-assembly process. We investigate the robustness of self-assembling bcc-type crystals via isotropic interaction potentials in binary systems with increasingly disparate particle sizes by determining their terminal size ratio—the most extreme size ratio at which a mixed binary bcc crystal forms. Our findings show that two-well pair potentials produce bcc crystals that are more robust with respect to particle size ratio than one-well pair potentials. This suggests that an improved self-assembly process is accomplished with a second attractive length scale encoded in the particle–particle interaction, which stabilizes the second-nearest neighbor shell. In addition, we document qualitative differences in the process of ordering and disordering: in bidisperse systems of particles interacting via one-well potentials, we observe a breakdown of order prior to demixing, while in systems interacting via two-well potentials, demixing occurs first and bcc continues to form in parts of the droplet down to low size ratios. 

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. 

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. 

This dataset accompanies the manuscript by J. J. Kennard, H. J. Zelaya Solano, R. C. Prager and J. Dshemuchadse, “Disorder and demixing in bidisperse particle systems assembling bcc crystals” J. Chem. Phys __(_), ____–____ (2024). We investigate the robustness of self-assembling bcc-type crystals via isotropic interaction potentials in binary systems with increasingly disparate particle sizes, by determining their terminal size ratio—the most extreme size ratio at which a mixed, binary bcc bcrystal forms. Our findings show that two-well pair potentials produce bcc crystals that are more robust with respect to particle size ratio than one-well pair potentials. Additionally we document qualitative differences in the process of ordering and disordering: in bidisperse systems of particles interacting via one-well potentials, we observe a breakdown of order prior to demixing, while in systems interacting via two-well potentials demixing occurs first and bcc continues to form in parts of the droplet down to low size ratios. This dataset includes 25,000 final simulation frames of the resulting crystal and amorphous 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. 

Metal–organic frameworks (MOFs) are crystalline materials that self-assemble from inorganic nodes and organic linkers, and isoreticular chemistry allows for modular and synthetic reagents of various sizes. In this study, a MOF’s components—metal nodes and organic linkers—are constructed in a coarse-grained model from isotropic beads, retaining the basic symmetries of the molecular components. Lennard-Jones and Weeks– Chandler–Andersen pair potentials are used to model attractive and repulsive particle interactions, respectively. We analyze the crystallinity of the self-assembled products and explore the role of modulators—molecules that compete with the organic linkers in binding to the metal nodes, and which we construct analogously—during the selfassembly process of defect-engineered MOFs. Coarse-grained simulation allows for the uncoupling of experimentally interdependent variables to broadly map and determine essential MOF self-assembly conditions, among which are properties of the modulator: binding strength, size (steric hindrance), and concentration. Of these, the simulated modulator’s binding strength has the most pronounced effect on the resulting MOF’s crystal size.