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This bundle consists of the main Python script, a readme.txt, a requirements_console_script.txt specifying the required packages, and the trained model modes as pickle les (pickle is a package for saving and loading trained algorithms). In the Python script, explanations for the correct input format are given along with examples. The code also enables multi-component input, which may be a fast and convenient option for some applications. The following pickle files (https://docs.python.org/3/library/pickle.html) are available: sm : SMILES Mode, requires SMILES string + melting temperature sm_no_tm : SMILES Mode without Tm, requires SMILES string fg_cho : Functional Group Mode for CHO compounds, requires functional groups + melting temperature fg_cho_no_tm : Functional Group Mode for CHO compounds without Tm, requires functional groups fg_nhal : Functional Group Mode for CHO compounds containing nitrogen or halogen atoms, requires functional groups + melting temperature fg_nhal_no_tm : Functional Group Mode for CHO compounds containing nitrogen or halogen atoms, requires functional groups For further information and contact please visit https://tgml.chemie.uni-bielefeld.de
If you use any of these data in your scientific work or in the resulting publications, please cite the corresponding original publication.
python, machine learning, molecular organics, glass transition temperature, Tg, prediction, SMILES, Bielefeld University, glass, amorphous solid, pickle
python, machine learning, molecular organics, glass transition temperature, Tg, prediction, SMILES, Bielefeld University, glass, amorphous solid, pickle
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