Polymer informatics: Current status and critical next steps

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