Report Summarizes Machine Learning Study Findings from George Washington Univers ity (Intricate Short-range Order In Gesn Alloys Revealed By Atomistic Simulation s With Highly Accurate and Efficient Machine-learning Potentials)

报告总结了乔治华盛顿大学的机器学习研究结果（原子模拟揭示了Gesn合金复杂的短程有序，具有高度精确和高效的机器学习潜力）

Robotics & Machine Learning Daily News2024，Issue(Jun.6) ：76-77.

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Report Summarizes Machine Learning Study Findings from George Washington Univers ity (Intricate Short-range Order In Gesn Alloys Revealed By Atomistic Simulation s With Highly Accurate and Efficient Machine-learning Potentials)

报告总结了乔治华盛顿大学的机器学习研究结果（原子模拟揭示了Gesn合金复杂的短程有序，具有高度精确和高效的机器学习潜力）

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摘要

机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据NewsRx编辑在哥伦比亚特区华盛顿的新闻报道，研究表明：“GeSn合金有望在电子学、光子学和拓扑量子器件中与硅兼容的集成应用。然而，利用密度泛函理论(DFT)计算理解它们复杂的结构受到时空常数的阻碍。”这项研究的资金支持者包括美国能源部(DOE)、美国能源部(DOE)、NERSC。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting out of Washington, District of Columbia, by NewsRx editors, research stated, “GeSn alloys hold promise for silicon-compatible integrated applications in electronics, photonics, and topolo gical quantum devices. However, understanding their intricate structures using d ensity functional theory (DFT) calculations is hindered by spatiotemporal constr aints.” Financial supporters for this research include United States Department of Energ y (DOE), United States Department of Energy (DOE), NERSC.

Key words

Washington/District of Columbia/United States/North and Central America/Alloys/Cyborgs/Emerging Technologies/Mach ine Learning/George Washington University

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出版年

2024

Robotics & Machine Learning Daily News

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