摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx记者从伊利诺伊州Lemont发回的新闻报道，研究表明：“了解复杂自旋织构的集体行为，如磁性天空子晶格，对于探索和控制这些S pin织构的紧急有序和诱导相变具有重要的心理意义。”这项研究的资助者包括基础能源科学。我们的新闻记者引用了阿贡国家实验室的研究：“理解Skyrmion-Skyrmion相互作用对于磁Skyrmion激活的水库或神经形态计算等应用也是至关重要的。磁Skyrmion晶格可以使用原位洛伦兹透射电子显微镜(LTEM)进行研究，但从LTEM图像中定量和统计稳健地分析Skyrmion晶格可能是困难的。在这项工作中，实验结果表明,在模拟数据上训练的卷积神经网络可以对自旋纹理进行分割,并从实验LTEM图像中提取不能手工获得的自旋纹理大小和位置等定量数据,其中包括关于Skyrmi大小、位置和形状的定量信息,而这些信息又可以反过来提取自旋纹理的大小和位置。我们将这种方法应用于Neel Skyrmion晶格的图像分割，从而可以准确地识别稠密和稀疏晶格中的Skyrmion si Ze和形变。
Abstract
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."
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