AI powered, automated discovery of polymer membranes for carbon capture
Creators
- 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
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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.
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References
Preprint (Preprint where the data is discussed) R. Giro et al., doi: 10.48550/arXiv.2206.14634
Journal reference (Paper in which the published data is described) R. Giro, et al, npj Computational Materials 9, 133 (2023), doi: 10.1038/s41524-023-01088-3