Review demonstrates molecular and polymer representations for machine learning models, indicating how hierarchy and stochasticity shape polymer informatics.
We review molecular and polymer representations for machine learning, from descriptors and fingerprints to learned embeddings, highlighting how hierarchy, stochasticity, and data limitations shape polymer informatics.
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Lin et al. (2026) studied this question.
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