Science Advances

Supplementary Materials

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 ntrain = 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 ΔCC-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)

Download PDF

Files in this Data Supplement: