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| Autori principali: | , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.13696 |
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| _version_ | 1866918161179213824 |
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| author | Simm, Gregor N. C. Hélie, Jean Schulz, Hannes Chen, Yicheng Simeon, Guillem Kuzina, Anna Martinez-Baez, Ernesto Gasparotto, Piero Tocci, Gabriele Chen, Chi Li, Yatao Cheng, Lixue Wang, Zun Nguyen, Bichlien H. Smith, Jake A. Sun, Lixin |
| author_facet | Simm, Gregor N. C. Hélie, Jean Schulz, Hannes Chen, Yicheng Simeon, Guillem Kuzina, Anna Martinez-Baez, Ernesto Gasparotto, Piero Tocci, Gabriele Chen, Chi Li, Yatao Cheng, Lixue Wang, Zun Nguyen, Bichlien H. Smith, Jake A. Sun, Lixin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles Simm, Gregor N. C. Hélie, Jean Schulz, Hannes Chen, Yicheng Simeon, Guillem Kuzina, Anna Martinez-Baez, Ernesto Gasparotto, Piero Tocci, Gabriele Chen, Chi Li, Yatao Cheng, Lixue Wang, Zun Nguyen, Bichlien H. Smith, Jake A. Sun, Lixin Chemical Physics 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. |
| title | SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2510.13696 |