![]() Kim, “Machine Learning and Materials Informatics: Recent Applications and Prospects”, npj Computational Materials 3, 54 (2017). Ramprasad, “Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions,” Journal of Physical Chemistry C, J. Ramprasad, “Scoping the Polymer Genome: A Roadmap for Rational Polymer Dielectrics Design and Beyond”, Materials Today, in press (2017). Challenges that remain are examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains are discussed. ![]() These efforts have culminated in the creation of an online Polymer Informatics platform ( ) to guide ongoing and future polymer discovery and design. Here, we describe our recent polymer discovery efforts, highlighting the role played by computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design.
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