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amorphous polymers

Amorphous polymers are polymeric materials whose molecular chains are randomly entangled and lack a regular, long-range crystalline order. Because of this disordered structure, these materials do not exhibit a distinct melting point; instead, they undergo a characteristic glass transition across a specific temperature range. Below their glass transition temperature, amorphous polymers are typically rigid, hard, and brittle, whereas above this temperature, molecular mobility increases and the material transitions into a flexible, rubbery, or viscous state. The absence of crystalline domains that scatter light frequently makes pure amorphous polymers optically transparent, with common examples including polystyrene, polycarbonate, and poly(methyl methacrylate).

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SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles

SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles

Gregor Simm, Jean Helie, Hannes Schulz, Yicheng Chen, Guillem Simeon, Anna Kuzina, Ernesto Martinez-Baez, Piero Gasparotto, Gabriele Tocci, Chi Chen, Yatao Li, Lixue Cheng, Zun Wang, Bichlien Nguyen, Jake Smith, Lixin Sun

OrganizationsMicrosoft

Why you should read this

Develops an ab initio machine learning force field that predicts bulk polymer densities and glass transition temperatures more accurately than classical force fields, accompanied by a 130-polymer experimental benchmark.

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. This work lays the foundation for a fully in silico design pipeline for next-generation polymeric materials.

Added

2026-09-29