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Automatized discovery of polymer membranes with AI generative design and molecular dynamics simulations

Ronaldo Giro1*, Hsianghan Hsu2, Akihiro Kishimoto2, Rodrigo F. Neumann1, Binquan Luan3, Seiji Takeda2, Lisa Hamada2, Mathias B. Steiner1

1 IBM Research Brazil - Avenida Rep├║blica 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:jk-zm [version v4]

Publication date: May 18, 2022

How to cite this record

Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto, Rodrigo F. Neumann, Binquan Luan, Seiji Takeda, Lisa Hamada, Mathias B. Steiner, Automatized discovery of polymer membranes with AI generative design and molecular dynamics simulations, Materials Cloud Archive 2022.65 (2022), doi: 10.24435/materialscloud:jk-zm.

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

No Explore or Discover sections associated with this archive record.

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.

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

Preprint (Preprint where the data is discussed)
R. Giro et al.

Keywords

Polymeric membranes CO2 separation Carbon dioxide separation