About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Designing High Glass Transition Temperature Polymers using Machine Learning |
Author(s) |
Chiho Kim, Rohit Batra, Lihua Chen, Huan Tran, Rampi Ramprasad |
On-Site Speaker (Planned) |
Chiho Kim |
Abstract Scope |
Machine learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. ML models are trained on predetermined data and then used to make predictions for new cases. Active-learning is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the next-best experiment. We demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperature (Tg). Starting from an initial small dataset, we use Gaussian process regression in conjunction with an active-learning framework to iteratively add candidate polymers to the training dataset. The active-learning workflow terminates once 10 polymers possessing Tg greater than a certain threshold temperature are selected. We statistically benchmark the performance of three strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the next-best experiment with respect to the discovery of high-Tg polymers for this particular demonstrative design challenge. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |