Wisconsin MRSEC researchers have developed a coarse-graining technique called AniSOAP (for anisotropic smooth overlap of atomic potentials) that gives the beads shapes that reflect the shape of the molecules they represent. This simple idea – carefully implemented to be mathematically rigorous and account for how molecules typically interact – can used for high-accuracy coarse grained simulations or to understand materials behavior that depends on molecular shape or orientation. AniSOAP is also particularly useful for machine learning analysis of molecular behavior using simple, physically-interpretable algorithms, producing new insight for researchers.

Wisconsin MRSEC researchers have developed a new way to see how molecules fit together with an electron microscope. They used the method to see how molecules rearrange when an organic semiconductor is heated. A modest change in temperature creates significantly improved molecular alignment. The improved alignment is reflected in both larger aligned regions and straighter lines of molecules inside each region.

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

Physical vapor deposition (PVD) canproduce glassthinfilmswith preferred orientation to the molecules andhigherdensitythan ordinaryliquid-quenchedglass bytakingadvantageofthefastmovementoforiented moleculesonthe glasssurface. Research supported by Wisconsin MRSEC have found a new way to control the structure and properties of these films by growing them on soft substrates. PVD on a soft substrate can produce glass thin films that are much more dense and stable than those deposited on rigid substrates. A film deposited on a soft substrate in 2 hours is equivalent to a film deposited extremely slowly on rigid substrates over ~3000 years.