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Active-learning and materials design: the example of high glass transition temperature polymers

Published online by Cambridge University Press:  13 June 2019

Chiho Kim Open the ORCID record for Chiho Kim [Opens in a new window] ,
Anand Chandrasekaran ,
Anurag Jha  and
Rampi Ramprasad
Chiho Kim
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA
Anand Chandrasekaran
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA
Anurag Jha
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA
Rampi Ramprasad*
Affiliation:
School of Materials Science and Engineering, Georgia Institute of Technology, 771 Ferst Drive NW, Atlanta, GA 30332, USA
*
Address all correspondence to Rampi Ramprasad at [email protected]
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Abstract

Machine-learning (ML) approaches have proven to be of great utility in modern materials innovation pipelines. Generally, ML models are trained on predetermined past data and then used to make predictions for new test cases. Active-learning, however, is a paradigm in which ML models can direct the learning process itself through providing dynamic suggestions/queries for the “next-best experiment.” In this work, the authors demonstrate how an active-learning framework can aid in the discovery of polymers possessing high glass transition temperatures (Tg). Starting from an initial small dataset of polymer Tg measurements, the authors use Gaussian process regression in conjunction with an active-learning framework to iteratively add Tg measurements of candidate polymers to the training dataset. The active-learning framework employs one of three decision making strategies (exploitation, exploration, or balanced exploitation/exploration) for selection of the “next-best experiment.” The active-learning workflow terminates once 10 polymers possessing a Tg greater than a certain threshold temperature are selected. The authors statistically benchmark the performance of the aforementioned three strategies (against a random selection approach) with respect to the discovery of high-Tg polymers for this particular demonstrative materials design challenge.

Type
Artificial Intelligence Research Letters
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 860 - 866
Copyright
Copyright © Materials Research Society 2019 

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Footnotes

†

Chiho Kim and Anand Chandrasekaran equally contributed to this work.

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