Science Advances

This PDF file includes:

• section 1. The atom-centered GAP is equivalent to the average molecular kernel
• section 2. A SOAP-GAP potential for silicon
  
• section 3. Predicting atomization energies for the GDB9 and QM7b databases
  
• section 4. Ligand classification and visualization
  
• table S1. Summary of the database for the silicon model.
  
• fig. S1. Energetics of configuration paths that correspond to the formation of stacking faults in the diamond structure.
  
• fig. S2. Fraction of test configurations with an error smaller than a given
  threshold, for n_train = 20,000 training structures selected at random (dashed line) or by FPS (full line).
  
• fig. S3. Optimal range of interactions for learning GDB9 DFT energies.
  
• fig. S4. Optimal range of interactions for learning GDB9 CC and Δ_CC-DFT energies.
  
• fig. S5. Training curves for the prediction of DFT energies using DFT geometries as inputs for the GDB9 data set.
  
• fig. S6. Training curves for the prediction of DFT energies using DFT geometries as inputs for the QM7b data set.
  
• fig. S7. Training curves for the prediction of DFT energies using DFT geometries as inputs for the GDB9 data set.
  
• fig. S8. Training curves for the prediction of DFT energies using DFT geometries as inputs, for a data set containing a total of 684 configurations of glutamic acid dipeptide (E) and aspartic acid dipeptide (D).
  
• fig. S9. Correlation plots for the learning of the energetics of dipeptide configurations, based on GDB9.
  
• References (44–68)

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