While our table contains properties that are available for most of the elements, there are some properties that have missing values for a variety of reasons. Some elements reside outside of the range of interest (lanthanides and actinides in the studies that focus on main-group elements) or outside the methodological approach (electronegativity values for inert gasses). In some instances, it was not clear why certain properties were not reported; therefore, we decided to predict them with a ML model. For this, we selected properties that contain missing values with less than 20% of the elements and the missing values were not grouped together in chemically similar groups (e.g., all lanthanides with missing values), but rather were scattered throughout the periodic table. In order to select an applicable method, a property with the most diverse elements with the missing values was selected. To test the approach, atomic radius, specifically Miracle radius was tested. To predict 6 missing Miracle radii values of the elements with the atomic numbers of 1, 2, 10, 18, 36, and 54, five supervised learning algorithms were used to generate regression models. The model was created for 83 elements. The features had 63 properties of elements, including different size scales (obviously without Miracle), electronegativities, and physical properties, and DFT-generated data. The algorithms included (1) Support-vector machines (SVR), (2) Decision Tree, (3) Multi-layer Perceptron neural network (MLP-NN), (4) Cubic spline interpolation, and (5) Gaussian process regression (GPR). The root mean squared error (RMSE) values between the actual and predicted Miracle values were compared. SVRs, MLP-NN, and Cubic Spline Interpolation resulted in high RMSE values of 30.7 to 37.6 (Figure ). The failure of the majority of the regression models can be attributed to the smoothness of the regression curves which failed to capture the dynamic rise and fall of radii values across the periods. The Decision Tree algorithm, in contrast, produced a RMSE value of 0, demonstrating a perfect alignment between actual and predicted Miracle values. However, each of the 6 predicted values was identical to the Miracle value of the adjacent element with one lower atomic number (chalcogens for inert gasses). For example, F had predicted Miracle radius 66 pm, and Neon (missing) also 66 pm. Out of the five algorithms, GPR with the Radial Basis Function (RBF) kernel produced the best regression model (Figure ). With the RMSE value of 4.95 ✕ 10 -11 , the unknown Miracles values were successfully interpolated with the GPR method.