首页|Rice University Reports Findings in Machine Learning (Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real- Time Crystal Morphology Evolutions)
Rice University Reports Findings in Machine Learning (Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real- Time Crystal Morphology Evolutions)
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New research on Machine Learning is the subject of a report. According to news reporting from Houston, Texas, by NewsRx journalists, research stated, “The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors.” The news correspondents obtained a quote from the research from Rice University, “Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology.”
HoustonTexasUnited StatesNorth and Central AmericaChemical Vapor DepositionCyborgsEmerging TechnologiesMachine Learning
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