Machine Learning Accelerates the Identification of Catalyst Performance (IMAGE)
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From DFT calculation to ML prediction, the potential catalysts with highly active and selective performance are efficiently screened by four ML models, i.e. decision tree, random forest, support vector machine, and XGBoost classification, where ten-fold cross-validation is employed to reduce overfitting risks during model training.
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Nano Research, Tsinghua University Press
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