About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Active Learning Guided Polymer Space Exploration and Discovery |
Author(s) |
Huan Tran, Abhirup Patra, Deepak Kamal, Lihua Chen, Chiho Kim, Rampi Ramprasad |
On-Site Speaker (Planned) |
Huan Tran |
Abstract Scope |
Two primary challenges in developing a computational polymer database are identifying the polymers to be considered and constructing reasonable crystal models for calculations. We have developed a rational scheme, which involves an active-learning guidance step and a polymer crystal structure prediction strategy, to solve these problems. Our scheme has been used to significantly enlarge a computational polymer database in a guided manner, promoting the diversity of the data in both the chemical space and the property space (taking polymer band gap as an example). Using the obtained data, we have developed a powerful multi-fidelity co-Kriging model for accurately and rapidly predicting polymer band gap. This model is available in the Polymer Genome online platform (https://www.polymergenome.org/). |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |