Polymer informatics: Current status and critical next steps

Chen, Lihua; Pilania, Ghanshyam; Batra, Rohit; Huan, Tran Doan; Kim, Chiho; Kuenneth, Christopher; Ramprasad, Rampi

doi:10.1016/j.mser.2020.100595

Title: Polymer informatics: Current status and critical next steps

Journal Article · Sat Dec 26 00:00:00 EST 2020 · Materials Science and Engineering. R, Reports

DOI:https://doi.org/10.1016/j.mser.2020.100595· OSTI ID:1825409

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Georgia Inst. of Technology, Atlanta, GA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Argonne National Lab. (ANL), Argonne, IL (United States). Center for Nanoscale Materials

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); Toyota Research Institute of North America; National Science Foundation (NSF); Alexander von Humboldt Foundation; Argonne National Laboratory, Laboratory Directed Research and Development (LDRD)

Grant/Contract Number:: 89233218CNA000001; AC02-06CH11357

OSTI ID:: 1825409

Alternate ID(s):: OSTI ID: 1781277; OSTI ID: 1831182

Report Number(s):: LA-UR-20-27820

Journal Information:: Materials Science and Engineering. R, Reports, Vol. 144; ISSN 0927-796X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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