Publication date: Jan 25, 2023
Data sets and scripts for computational discovery of polymer membranes for carbon dioxide separation. The training data set with 1,169 homo-polymers provides carbon dioxide permeability, glass transition temperature and half decomposition temperature for each listed material. The output data set contains 784 optimized homo-polymer candidates generated by Inverse Design and Machine Learning techniques. The Jupyter notebook enables the use of the Polymer Property Prediction Engine as a service for generating the properties provided in the training data set.
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File name | Size | Description |
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ppf_API_demo.ipynb
MD5md5:468d276cf957d2636c5876ac76f9f973
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11.1 KiB | Jupyter notebook with an example of how to use the Polymer Property Prediction Engine with APIs (application programming interface) |
example.csv
MD5md5:77a1abfca19c1a53bfc954c0372338b6
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550 Bytes | Input file data-set for the Jupyter notebook |
PCO2-Tg-Thd-data-all-simulated.csv
MD5md5:534cf22962db6838cb93cd023a1c2306
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189.1 KiB | 1,169 calculated homo-polymers properties: IUPAC polymer name, OPSIN SMILES, log10( CO2 permeability (Barrer)), glass transition temperature (K) and half decomposition temperature (K) |
results_monomers_generative_model.csv
MD5md5:50e094dcc18de52b03a67cc6d4e8a6c9
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81.7 KiB | 784 homopolymers generated by Inverse Design and Machine Learning techniques with the OPSIN SMILES, glass transition temperature (Tg), half decomposition temperature (Thf) and CO2 permeability (PCO2). Tg and Thf are calculated using Polymer Property Prediction Engine, and PCO2 was calculated by the Machine Learning regression model. |
model_molgx.ipynb
MD5md5:f9b879f150fb863789f943d42b09db66
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291.6 KiB | Jupyter notebook for the training and regression models with the open-source version of IMD engine which is available at: https://github.com/GT4SD/molgx-core/ |
2023.20 (version v7) | Jan 30, 2023 | DOI10.24435/materialscloud:64-c8 |
2023.14 (version v6) [This version] | Jan 25, 2023 | DOI10.24435/materialscloud:ma-eq |
2022.99 (version v5) | Jul 27, 2022 | DOI10.24435/materialscloud:ma-qn |
2022.65 (version v4) | May 18, 2022 | DOI10.24435/materialscloud:jk-zm |
2022.61 (version v3) | May 10, 2022 | DOI10.24435/materialscloud:p8-ey |
2022.58 (version v2) | Apr 28, 2022 | DOI10.24435/materialscloud:zm-hb |
2022.56 (version v1) | Apr 27, 2022 | DOI10.24435/materialscloud:ss-fq |