首页|Argonne National Laboratory Researcher Provides Details of New Studies and Findi ngs in the Area of Machine Learning (Simulationtrained machine learning models for Lorentz transmission electron microscopy)
Argonne National Laboratory Researcher Provides Details of New Studies and Findi ngs in the Area of Machine Learning (Simulationtrained machine learning models for Lorentz transmission electron microscopy)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting originating from Lemont, Illinois, by NewsRx correspondents, research stated, "Understanding the collective behavi or of complex spin textures, such as lattices of magnetic skyrmions, is of funda mental importance for exploring and controlling the emergent ordering of these s pin textures and inducing phase transitions." Funders for this research include Basic Energy Sciences. Our news correspondents obtained a quote from the research from Argonne National Laboratory: "It is also critical to understand the skyrmion-skyrmion interactio ns for applications such as magnetic skyrmionenabled reservoir or neuromorphic computing. Magnetic skyrmion lattices can be studied using in situ Lorentz trans mission electron microscopy (LTEM), but quantitative and statistically robust an alysis of the skyrmion lattices from LTEM images can be difficult. In this work, we show that a convolutional neural network, trained on simulated data, can be applied to perform segmentation of spin textures and to extract quantitative dat a, such as spin texture size and location, from experimental LTEM images, which cannot be obtained manually. This includes quantitative information about skyrmi on size, position, and shape, which can, in turn, be used to calculate skyrmion- skyrmion interactions and lattice ordering. We apply this approach to segmenting images of Neel skyrmion lattices so that we can accurately identify skyrmion si ze and deformation in both dense and sparse lattices."
Argonne National LaboratoryLemontIll inoisUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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