2025/09/04 by Lionel Zoubritzky, François‐Xavier Coudert · 1 voice
Materials Science · Chemical Engineering · #Machine Learning in Materials Science #Catalysis and Oxidation Reactions #X-ray Diffraction in Crystallography
paper · pdf · doi:10.26434/chemrxiv-2025-bmthz
Because topology plays a key role in many chemical and physical properties of materials, identification of topology from crystalline structures is a common and important task in materials science. We present here a new web application, CrystalNets, whose user-friendly interface allows scientists to identify and visualize the topology of crystals from their atomic structure in CIF format. The software has a lot of options to customize its features, such as detection of bonding, choice of clustering model, type of materials (inorganic, hybrid, etc). It has a default mode with powerful heuristics, and allows the user to easily visualize and check the topology detected against the full structure. We also improved the underlying CrystalNets.jl Julia library, including a systematic algorithm to handle the complex case of unstable nets.