Fig. 4: Selection of top predictor properties and the resulting model performance using the pool of 153 IPs.
From: Cross-scale covariance for material property prediction
a Heat map showing which generic predictor groups (e.g., “elastic const” includes C11, C12, and C44 for FCC, BCC, and SC) are used in the top-performing regression models, as determined by repeated k-fold cross-validation. A filled box means at least one predictor from that group was used in the model. The color map at the top represents the root mean square error (RMSE) in the predicted strength in MPa. See the SI for factors included in each group. b Comparison of the strength predicted by a top multi-linear regression model with the strength obtained from a large-scale MD simulation. The regression model is based on three predictors: {111} surface energy, lattice constant, and vacancy migration energy, all for FCC. The RMSE of the regression model is estimated using leave-one-out cross-validation, see the text for details.
