The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials and chemical formulations design. In an increasing number of applications, the successful deployment of novel materials and chemical formulations has benefited from the use of computational and informatics methodologies coupled with experiment. Here, I will use the polymer informatics ecosystem—pioneered in the Ramprasad group—as an example of how artificial intelligence (AI) and machine learning (ML) methods and philosophies may be used to accelerate the development of new materials and chemistries. I will describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction, algorithms for the inverse design of polymers meeting target property requirements, and finally, how prior physics knowledge can be woven into polymer informatics workflows. 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. 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 materials, chemistries and processing conditions, and the applicability of emerging AI algorithms for materials design.

Polymer genome: Accelerating materials discovery via data-driven approaches

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ACS Spring 2023

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