Active-learning and materials design: the example of high glass transition temperature polymers
- PDF / 602,740 Bytes
- 7 Pages / 612 x 792 pts (letter) Page_size
- 55 Downloads / 191 Views
Artificial Intelligence Research Letter
Active-learning and materials design: the example of high glass transition temperature polymers Chiho Kim †, Anand Chandrasekaran†, Anurag Jha, and Rampi Ramprasad, 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] (Received 16 January 2019; accepted 29 May 2019)
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.
Introduction In order to design new materials for specific applications, we often have to search for materials which possess a given set of properties within a required window. For example, design of polymers for energy storage applications requires that such materials possess simultaneously high dielectric constant and bandgap.[1–9] Another example is the design of solid polymer electrolytes for Li-ion batteries. Such materials are required to possess a suitably high Li-ion conductivity[10] in conjunction with an appropriate electrochemical window.[11] In order to guide this search for materials with promising functionalities, scientists and researchers often rely on intuition gained from past experiments to design the next set of experiments. Even so, how does one decide whether to continue searching within a particularly promising class of materials or switch to searching for candidates in a more unexplored region of chemical or structural space? Rather than making such decisions based purely on human intuition, active-learning algorithms that exploit Bayesian optimization (BO) frameworks may be utilized.[12–15] Over the past decade, machine-learning (ML)-based algorithms and techniques have been of tremendous utility in
Data Loading...
