Machine-learning predictions of polymer properties with Polymer Genome
Tran Doan Huan, Chiho Kim, Lihua Chen, Anand Chandrasekaran, Rohit Batra, Shruti Venkatram, Deepak Kamal, Jordan P. Lightstone, Rishi Gurnani, Pranav Shetty, Manav Ramprasad, Julia Laws, Madeline Shelton, Rampi Ramprasad
Abstract
Polymer Genome is a web-based machine-learning capability to perform near-instantaneous predictions of a variety of polymer properties. The prediction models are trained on (and interpolate between) an underlying database of polymers and their properties obtained from first principles computations and experimental measurements. In this contribution, we first provide an overview of some of the critical technical aspects of Polymer Genome, including polymer data curation, representation, learning algorithms, and prediction model usage. Then, we provide a series of pedagogical examples to demonstrate how Polymer Genome can be used to predict dozens of polymer properties, appropriate for a range of applications. This contribution is closed with a discussion on the remaining challenges and possible future directions.