Ronaldo Giro^1*, Hsianghan Hsu², Akihiro Kishimoto², Toshiyuki Hama², Rodrigo F. Neumann¹, Binquan Luan³, Seiji Takeda², Lisa Hamada², Mathias B. Steiner¹

1 IBM Research Brazil - Avenida República do Chile, 330 - 11o. e 12. andares Rio De Janeiro, RJ 20031-170, Brazil

2 IBM Research Tokyo - 7-7 Shinkawasaki Saiwai-Ku Kawasaki, 14 212-0032, Japan

3 IBM Research - 1101 Kitchawan Rd PO Box 218 Yorktown Heights, NY 10598-0218, USA

* Corresponding authors emails: rgiro@br.ibm.com

DOI10.24435/materialscloud:64-c8 [version v7]

Publication date: Jan 30, 2023

How to cite this record

Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto, Toshiyuki Hama, Rodrigo F. Neumann, Binquan Luan, Seiji Takeda, Lisa Hamada, Mathias B. Steiner, AI powered, automated discovery of polymer membranes for carbon capture, Materials Cloud Archive 2023.20 (2023), https://doi.org/10.24435/materialscloud:64-c8

Description

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.

Materials Cloud sections using this data

Files

File name	Size	Description
ppf_API_demo.ipynb MD5md5:468d276cf957d2636c5876ac76f9f973	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	550 Bytes	Input file data-set for the Jupyter notebook
PCO2-Tg-Thd-data-all-simulated.csv MD5md5:534cf22962db6838cb93cd023a1c2306	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	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	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/

License

Files and data are licensed under the terms of the following license: Materials Cloud non-exclusive license to distribute v1.0. CDLA-Sharing-1.0
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

Polymeric membranes CO2 separation Carbon dioxide separation