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Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
Yuan Zhou, Bing Chen, Hongyu He, Bo Li, Xinlin Wang
Nuclear Technology | Volume 206 | Number 1 | January 2020 | Pages 32-39
Technical Paper | doi.org/10.1080/00295450.2019.1613850
Articles are hosted by Taylor and Francis Online.
With large-scale molecular dynamics, we investigate displacement cascades in monocrystalline silicon with regard to the effects of temperature, strain, and primary knock-on atom energy on defect generation and evolution. With temperature increasing, both the thermal spike region and the peak defect count increase, while the effect of temperature on the surviving defect number is negligible. Nevertheless, higher temperature shows negative effect on clustering of vacancy. The effects of uniaxial strain on defect production and clustering is negligible, while its hydrostatic counterpart is evident. With the increment of hydrostatic strain, both the peak and surviving defect count increase (decrease) under tensile (compressive) hydrostatic loading. Meantime, tensile hydrostatic strain will promote defect clustering. More defects and larger defect clusters are produced at higher energy. Otherwise, interstitials are hard to form clusters under different conditions.