A Stereochemical Blueprint for Thermoresponsive Polymers
Abstract
Predicting polymer structure-property relationships is challenging due to the vast chemical expanse of polymer design space and the limited availability of high-quality datasets that effectively span the design space. Here, we present a physics-informed framework that integrates high-throughput molecular dynamics (MD) simulations with machine learning to predict the lower critical solution temperature (LCST) of stereochemically diverse poly(Nisopropylacrylamide) (PNIPAM) systems using 5632 MD simulation trajectories, corresponding to a cumulative simulation time of 2816 µs. We use validated MD simulations to generate descriptors that capture local hydration and conformational environments, enabling the extraction of molecular features directly linked to thermoresponsive behaviour. The resulting model accurately reproduces subtle experimentally observed LCST variations arising from changes in tacticity, demonstrating that sequence derived features and simulation-derived hydration descriptors encode the molecular information required to resolve compositiondependent phase behaviour. Beyond LCST prediction, the framework provides mechanistic insight into the molecular origins of thermoresponsive behaviour of PNIPAM, revealing that meso and racemic dyads undergo distinct backbone rotational mediated collapse pathways, with meso dyads requiring backbone rotation through partially hydrated intermediates, whereas racemic dyads collapse within a fixed backbone rotational conformation through closure of the isopropyl-isopropyl distance. These findings establish a transferable simulation-machinelearning strategy for linking polymer structural information to macroscopic properties and accelerating the discovery of functional polymer materials.
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BibTeX
@misc{munaweera2026stereochemical,
title = {{A Stereochemical Blueprint for Thermoresponsive Polymers}},
author = {Rangika Munaweera and Martin Stroet and Megan L. O'Mara},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15009848/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009848/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A Stereochemical Blueprint for Thermoresponsive Polymers
AU - Rangika Munaweera
AU - Martin Stroet
AU - Megan L. O'Mara
PY - 2026
DA - 2026/10/05
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009848/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009848/v1
AB - Predicting polymer structure-property relationships is challenging due to the vast chemical expanse of polymer design space and the limited availability of high-quality datasets that effectively span the design space. Here, we present a physics-informed framework that integrates high-throughput molecular dynamics (MD) simulations with machine learning to predict the lower critical solution temperature (LCST) of stereochemically diverse poly(Nisopropylacrylamide) (PNIPAM) systems using 5632 MD simulation trajectories, corresponding to a cumulative simulation time of 2816 µs. We use validated MD simulations to generate descriptors that capture local hydration and conformational environments, enabling the extraction of molecular features directly linked to thermoresponsive behaviour. The resulting model accurately reproduces subtle experimentally observed LCST variations arising from changes in tacticity, demonstrating that sequence derived features and simulation-derived hydration descriptors encode the molecular information required to resolve compositiondependent phase behaviour. Beyond LCST prediction, the framework provides mechanistic insight into the molecular origins of thermoresponsive behaviour of PNIPAM, revealing that meso and racemic dyads undergo distinct backbone rotational mediated collapse pathways, with meso dyads requiring backbone rotation through partially hydrated intermediates, whereas racemic dyads collapse within a fixed backbone rotational conformation through closure of the isopropyl-isopropyl distance. These findings establish a transferable simulation-machinelearning strategy for linking polymer structural information to macroscopic properties and accelerating the discovery of functional polymer materials.
ER -