Prediction of Molecular Excitation by Random Forest

Beomchang Kang*, Chaok Seok*, Juyong Lee**

*Seoul National University

**Kangwon National University

User guide

These models are the result of training for prediction of electronic transition as described in Kang et al., "Prediction of Molecular Electronic Transitions Using Random Forests".
PredMolElecTranRF.zip contains five files.
  • First, prediction.py is executable file.
  • Then, InputFile and InputFile.out are examples of input and output file, respectively. Input file is a list of SMILES formats. In output file, the first column represents the maximum oscillator strength and the second column represents its excitation energy. If a SMILES format is chemically invalid, first and second column represent 'invalid'.
  • Finally, os_parameter ee_parameter are parameter files for random forest prediction. 
usage: prediction.py [-h] [--Input INPUT]

Download

References

  1. Kang, Beomchang; Seok, Chaok; Juyong Lee. Prediction of Molecular Electronic Transitions Using Random Forests, J. Chem. Inf. Model, 60 (12), 5984-5994 (2020).

Contact

juyong.lee@kangwon.ac.kr