8 ADVANCED MATERIALS & PROCESSES | SEPTEMBER 2026 NEW MODEL BETTER PREDICTS ALLOY BEHAVIOR Researchers at MIT created a technique that captures chemical arrangements across materials to improve predictions of how metal alloys and other complex materials will behave. Central to their motif-based approach are machine-learning models that make simulations of materials faster and more accurate. The researchers improved those models by building training datasets that capture the wide range of atomic environments in chemically disordered materials. The team showed their method could be used to accurately predict material properties for a diverse group of metal alloys under a variety of conditions. They also demonstrated how the approach could be used to develop new materials, especially in scenarios where experimentation is expensive. The group says their method can be adapted for many different materials, from semiTESTING | CHARACTERIZATION AUTOMATED DEFECT DETECTION FOR DIAMOND Scientists at Rice University, Houston, developed a new workflow methodology for measuring microscopic defects in diamond and other advanced semiconductor materials. By making it easier to spot flaws that can undermine performance, they say the approach could accelerate development of more reliable electronic and quantum devices. The team developed a custom Python-based software tool to rapidly analyze data from high- resolution x-ray diffraction. The soft- ware analyzes the resulting diffraction patterns, identifies dislocations and irregularities in the atomic lattice, and calculates their density in each material. The new framework is especially suited to measuring dislocation density in diamond and other wide-bandgap semiconductors. This family of materials can handle more heat and electrical stress than silicon, making them attractive for applications such as electric vehicle power systems and power grid infrastructure. Yet measuring crystal quality remains a challenge. While similar x-ray-based methods are widely used for other semiconductor materials, applying them to diamond has proven more difficult because diamond’s crystal structure and defect behavior are vastly different from other materials. To test the new framework, the team analyzed four commercially available grades of single-crystal diamond with varying levels of crystal quality. The automated workflow clearly distinguished among the materials, identifying electronic- grade diamond as having the lowest defect density and most uniform crystal quality. Heteroepitaxial diamond, which is grown on a non- diamond substrate, exhibited the highest defect density and greatest structural disorder. Different techniques used to validate the results showed consistent trends, supporting the reliability of the approach. The researchers plan to continue refining the methodology and expand the range of materials and defect types it can analyze. rice.edu. Tia Gray, now a Rice doctoral alumna, is first author in the study published in Advanced Materials. Courtesy of Brandon Martin/Rice University. Tufts University opened a materials imaging service called Cocoon that provides state-of-the-art tools to industry and academic clients. Instruments include optical microscopy, laser confocal imaging, Raman spectroscopy, EDX microscopy, scanning electron microscopy, and atomic force microscopy. tufts.edu. Shimadzu Corp., Japan, completed its acquisition of Tescan, Czech Republic, on July 7. Tescan manufactures electron microscopy equipment for materials science, life sciences, semiconductor research, industrial R&D, and quality control. tescan.com. BRIEFS This graphic compares a random sampling approach of chemical arrangements across materials to the researchers’ new motif-based sampling. Courtesy of the MIT researchers.
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