Science Advances

Supplementary Materials

This PDF file includes:

  • Fig. S1. Temperature transferability of the ANN-ECG model.
  • Fig. S2. ANN-ECG performance versus training data aize for 500 K/rigid dataset.
  • Fig. S3. Distribution of HOMO energy levels for 300 K/flexible and 300 K/rigid datasets.
  • Fig. S4. Atomic numbering scheme used for each 3MT monomer.
  • Fig. S5. Delta–machine learning fitting results for ANN-ECG using 300 K/rigid dataset.
  • Fig. S6. Application of ANN-ECG to conjugated copolymer PTB7 and non-fullerene acceptor TPB.
  • Fig. S7. ANN-ECG results for the HOMO-5→HOMO energy levels of S3MT using 300 K/rigid dataset computed at the BP86/6-31G* level of theory.
  • Table S1. Hyperparameter optimization for ANN layers and neurons.
  • Table S2. Hyperparameter optimization for number of training epochs.
  • Table S3. Results using ANN-ECG and a systematic coarse-graining strategy.

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