Tag: IRG 2

Wisconsin MRSEC researchers in IRG 2 have discovered that the compound TaIrTe4, a transition metal dichalcogenide, when made only a few atomic layers thick and cooled to cryogenic temperatures, transitions into an unusual state called a one-dimensional charge density wave. A charge density wave state exhibits variation in the local density of electrons over distances significantly longer than the distances between the atoms.

Wisconsin MRSEC IRG 2 researchers have developed an efficient, accurate first-principles computational method that predicts how magnons and atomic vibrations (phonons) couple inside magnetic materials — a calculation that previously required prohibitively expensive computations. By predicting this coupling from the chemical composition of a material alone, with no fitted parameters, the new method lets researchers screen and design materials with longerlasting, higher performing spin waves before any sample is grown.

Wisconsin MRSEC IRG 2 members Daniel Rhodes and Yangchen He are commercializing new materials for advanced computing developed in part with MRSEC support. With support from the NSF I-Corps program and the Wisconsin MRSEC Advanced Materials Industrial Consortium, Rhodes and He have founded Pristine Quantum Inc. The company is based on their inventions of ultra low defect 2D semiconductor crystals and a method to print them directly onto industry-compatible substrates.

Wisconsin MRSEC researchers have demonstrated that strain can dramatically alter the magnetoelastic properties of a two-dimensional material, CrSBr. Magnetoelasticity is the interaction between magnetism and strain. The researchers developed a nanoscale mechanical resonator device to measure the material’s magnetoelastic coupling. Using it, they showed that 2D CrSBr has a particularly large coupling, and that it can be tuned by 50% by stretching the 2D membrane.

Wisconsin MRSEC researchers have create an artificially magnetic material by alternating layers of Mn3GaN and Mn3Ga in perfect atomic registry with one another. The resulting material offers best-of-both worlds performance for advanced electronic devices based on spin. Their magnetism is easy to switch to encode information, but it is stable once set to a specific state. These outstanding properties arise both from the properties of the layers themselves and from the unique atomic environments that exist where the two layers meet. As a result, combining materials this way lets materials scientists design materials that cannot otherwise exist.

Polycrystalline materials are everywhere in everyday life, but their microstructure – the arrangement of atoms into crystal grains and grains into a piece of material – covers 10 orders of magnitude in size and involves millions important features. This complexity makes it extremely difficult for scientists to predict the properties of polycrystalline materials quickly and accurately. Wisconsin MRSEC researchers have leveraged the power of machine learning to tame the complexity of polycrystalline materials and predict their properties. They have developed a graph neural network approach that predicts materials properties with >98% accuracy 90,000 times faster than competing methods. They applied this model to predict magnetostriction, which quantifies the size change of a material induced by a magnetic field. Development and design of high magnetostriction materials will enable MRSEC researchers to efficiently control magnetism using mechanical force and enable future technologies such like magnetic soft robots