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Polymer informatics : Current status and critical next steps

Title data

Chen, Lihua ; Pilania, Ghanshyam ; Batra, Rohit ; Huan, Tran Doan ; Kim, Chiho ; Künneth, Christopher ; Ramprasad, Rampi:
Polymer informatics : Current status and critical next steps.
In: Materials Science and Engineering: R: Reports. Vol. 144 (2021) . - 100595.
ISSN 0927-796X
DOI: https://doi.org/10.1016/j.mser.2020.100595

Abstract in another language

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, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Engineering Science > Juniorprofessur Computational Materials Science > Juniorprofessur Computational Materials Science - Juniorprof. Dr. Christopher Künneth
Result of work at the UBT: No
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 05 May 2023 08:56
Last Modified: 05 May 2023 08:56
URI: https://eref.uni-bayreuth.de/id/eprint/76147