摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx记者从华盛顿大学传来的消息，研究表明："在最近对R型扭曲MoTe 2和WSe2同层中相反的Chern数的实验观察的激励下，我们用机器学习力场进行了大规模的密度泛函理论计算，以研究两种材料中具有一定扭转角的莫尔带拓扑。"新闻记者从瓦辛顿大学的研究中获得了一句话：“我们发现莫尔前沿带的Chern数随着扭转角的变化而变化，这种变化是由莫尔铁电性和压电电性之间的竞争所驱动的。我们借助机器学习方法进行的大规模计算揭示了扭曲双层系统中跨不同尺度相互作用的关键见解。”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from the Uni versity of Washington by NewsRx correspondents, research stated, “Motivated by r ecent experimental observations of opposite Chern numbers in R-type twisted MoTe 2 and WSe2 homobilayers, we perform large-scale density-functional-theory calcul ations with machine learning force fields to investigate moire band topology acr oss a range of twist angles in both materials.” The news correspondents obtained a quote from the research from University of Wa shington: “We find that the Chern numbers of the moire frontier bands change sig n as a function of twist angle, and this change is driven by the competition bet ween moire ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions a cross different scales in twisted bilayer systems.”
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