首页|University of Washington Researchers Target Machine Learning (Polarization-drive n band topology evolution in twisted MoTe2 and WSe2)

University of Washington Researchers Target Machine Learning (Polarization-drive n band topology evolution in twisted MoTe2 and WSe2)

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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.”

University of WashingtonCyborgsEmerg ing TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.4)