Search for: All records

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. 

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.