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Supporting Files to Machine Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Authors: Armeli Iapichino, Gianluca; Peters, Jan-Hendrik; Koop, Thomas;

Supporting Files to Machine Learning-Based Prediction of the Glass Transition Temperature of Organic Compounds Using Experimental Data

Abstract

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.

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Keywords

python, machine learning, molecular organics, glass transition temperature, Tg, prediction, SMILES, Bielefeld University, glass, amorphous solid, pickle

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