Polymer Informatics: Current Status and Critical Next Steps
Lihua Chen, Ghanshyam Pilania, Rohit Batra, Tran Doan Huan, Chiho Kim,, Christopher Kuenneth, Rampi Ramprasad

TL;DR
This paper reviews the current state of polymer informatics, highlighting AI-driven methods for polymer design, data challenges, and future opportunities for accelerating polymer discovery and synthesis.
Contribution
It provides a comprehensive overview of recent advances, challenges, and future directions in applying AI and machine learning to polymer informatics.
Findings
AI models enable rapid property prediction for polymers
Data scarcity and representation are major hurdles
Inverse design approaches are emerging for targeted polymer synthesis
Abstract
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…
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