Polymer Informatics—Beyond Homopolymers

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction models, and how such methods will have to evolve to handle situations including homopolymers, copolymers and blend polymers. These efforts have culminated in the creation of an online Polymer Informatics platform (https://www.polymergenome.org) to guide ongoing and future polymer discovery and design [1-3]. Challenges that remain will be examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains will be discussed. These include strategies to deal with the data bottleneck, new methods to represent polymer morphology and processing conditions, and the applicability of emerging AI algorithms for materials design.<br/>[1] Rohit Batra, Le Song and Rampi Ramprasad, “Emerging materials intelligence ecosystems propelled by machine learning”, <i>Nature Reviews Materials</i> (2020)<br/>[2] Huan Doan Tran, 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, and Rampi Ramprasad, “Machine-learning predictions of polymer properties with Polymer Genome”, <i>Journal of Applied Physics</i> (2020).<br/>[3] Christopher Kuenneth, William Schertzer, and Rampi Ramprasad, Copolymer Informatics with Multitask Deep Neural Networks, <i>Macromolecules</i> (2021).