  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.
