Themed collection Data Science and Machine Learning in Polymer Research
Basic concepts and tools of artificial intelligence in polymer science
AI-driven polymer science: a structured perspective on integrating machine learning for data analysis, property prediction, and automated research workflows.
Polym. Chem., 2025,16, 2457-2470
https://doi.org/10.1039/D5PY00148J
Controlled synthesis and post-modification of polypentafluorostyrene in continuous flow
This article presents a kinetic study on the RAFT polymerisation of pentafluorostyrene as well as the subsequent precise control over the post-polymerisation modification via para-fluoro thiol reaction by virtue of continuous flow.
Polym. Chem., 2026, Advance Article
https://doi.org/10.1039/D5PY01142F
Prompt2Poly: ask, specify, create – a dialogue-based large language model for targeted TSMPs design
Prompt2Poly, a natural language-driven framework, generates thermoset shape memory polymers in response to user prompts by specifying or clarifying target properties through iterative reasoning.
Polym. Chem., 2025,16, 4918-4933
https://doi.org/10.1039/D5PY00921A
Molecular dynamics as a tool for unveiling protein-like folding behavior in urethane-based macromolecules: the effect of chain length on the secondary structure of oligourethanes
Sequence-defined and stereocontrolled polymers represent an emerging class of macromolecules, offering the potential to design protein-like molecular architectures based on abiotic polymer backbones.
Polym. Chem., 2025,16, 4622-4636
https://doi.org/10.1039/D5PY00635J
Polymer composites informatics for flammability, thermal, mechanical and electrical property predictions
Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups.
Polym. Chem., 2025,16, 3459-3467
https://doi.org/10.1039/D4PY01417K
Online GPC monitoring for batch and flow polymerisation reactions
Gel Permeation Chromatography (GPC) is well established as the gold standard for routine molecular weight analysis of polymers giving both mass and mass dispersity information.
Polym. Chem., 2025,16, 3329-3343
https://doi.org/10.1039/D5PY00554J
Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics
A self-driving laboratory, combining automated synthesis, characterisation, and cloud-based AI, was developed to optimise the synthesis of polymer nanoparticles by RAFT dispersion polymerisation.
Polym. Chem., 2025,16, 1355-1364
https://doi.org/10.1039/D5PY00123D
Optimisation of azide–alkyne click reactions of polyacrylates using online monitoring and flow chemistry
Herein, the online and inline 1H-NMR monitoring of azide–alkyne click reactions of polymers is investigated.
Polym. Chem., 2025,16, 1065-1071
https://doi.org/10.1039/D4PY00984C
About this collection
From accelerating materials discovery and optimizing polymer synthesis to predicting structure-property relationships and enabling advanced characterization, AI/ML is rapidly becoming an indispensable tool for researchers. This special issue, Guest Edited by Professor Nicholas Warren (University of Sheffield, UK) and Professor Ying Li (University of Wisconsin-Madison, USA) delves into these advancements, covering key areas such as:
- Polymer Design and Discovery
- Polymer Synthesis
- Polymer Materials Properties
- Polymer Characterisation
- Theoretical and Computational Polymer Science
More articles will be added to the collection as they are published.