https://www.selleckchem.com/products/tp-0903.html
Machine learning classification is a useful technique to predict structure/property relationships in samples of nanomaterials where distributions of sizes and mixtures of shapes are persistent. The separation of classes, however, can either be supervised based on domain knowledge (human intelligence), or based entirely on unsupervised machine learning (artificial intelligence). This raises the questions as to which approach is more reliable, and how they compare? In this study we combine an ensemble data set of electronic structure simu
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