SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles + the Challenge of Long-Range Interactions (arXiv:2510.13696)

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• Dr Lixin Sun and Dr Gregor Simm, Microsoft
• Wednesday 04 March 2026, 14:30-15:30
• Unilever Lecture Theatre, Yusuf Hamied Department of Chemistry.

If you have a question about this talk, please contact Lisa Masters.

Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale nature poses significant modeling challenges. Conventional force fields often lack the accuracy and transferability required to capture the intricate interactions governing polymer behavior. Conversely, quantum-chemical methods are computationally prohibitive for the large systems and long timescales required to simulate relevant polymer phenomena. Here, we overcome these limitations with a machine learning force field (MLFF) approach. We demonstrate that macroscopic properties for a broad range of polymers can be predicted ab initio, without fitting to experimental data. Specifically, we develop a fast and scalable MLFF to accurately predict polymer densities, outperforming established classical force fields. Our MLFF also captures second-order phase transitions, enabling the prediction of glass transition temperatures. To accelerate progress in this domain, we introduce a benchmark of experimental bulk properties for 130 polymers and an accompanying quantum-chemical dataset. In this talk, we also discuss the persistent of learning long-range non-covalent interactions in MLFF , best practices, and potential future development.

This talk is part of the Theory - Chemistry Research Interest Group series.

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