首页|Data on Machine Learning Described by Researchers at University of Toronto (Machine Learning Enabled Prediction of High Stiffness 2d Materials)
Data on Machine Learning Described by Researchers at University of Toronto (Machine Learning Enabled Prediction of High Stiffness 2d Materials)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news reporting originating from Toronto, Canada, by NewsRx correspondents, research stated, “Persistent ex- ploration of high stiffness two-dimensional (2D) materials is necessary for advancements in applications such as nanocomposites, flexible electronics, and resonant sensors, all of which demand elevated resistance to deformation. However, data-centric material models developed for this purpose remain in their early stages, often due to incomplete stiffness estimation or limited transferability to unseen 2D materials.” Funders for this research include University of Toronto, CGIAR, University of Toronto. Our news editors obtained a quote from the research from the University of Toronto, “In this context, we examined stiffness trends among different classes of 2D materials and identified the elastic constants pivotal for estimating the 2D material stiffness irrespective of their crystal symmetry. Subsequently, we developed Gaussian Process Regression machine learning models with the capability of relative stiffness comparison, which are used to predict high stiffness candidates across a broad spectrum of unseen 2D materials during model training. The probability of finding high stiffness 2D materials increased significantly, from a mere 1% in the training data set to a notable 47% in the set of machine learning-predicted 2D materials.” According to the news editors, the research concluded: “We also discussed potential stiffening mecha- nisms, competing stiffness characteristics, and complementary properties of these predicted high-stiffness 2D materials that are crucial for enhancing the effectiveness of the aforementioned applications.” This research has been peer-reviewed.
TorontoCanadaNorth and Central AmericaCyborgsEmerg- ing TechnologiesMachine LearningUniversity of Toronto
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