Optimizing Downsampling in Variable Density Experimental Data: Predicting Metallic Glasses
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چکیده
Metallic glasses remain an elusive material type, due to the lack of analytical formulae or empirical models for their prediction. However, recent improvements in experimental methods have provided high-density alloy data, making it possible to apply machine learning techniques for prediction of metallic glasses. In this paper, we consider a variety of machine learning algorithms for predicting metallic glasses and investigate the most efficient way to incorporate high-density experimental data into existing sparse datasets.
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