Predicting structure and swelling of microgels with different crosslinker concentrations combining machine-learning with numerical simulations
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916608289538048 |
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| author | Marín-Aguilar, Susana Zaccarelli, Emanuela |
| author_facet | Marín-Aguilar, Susana Zaccarelli, Emanuela |
| contents | Microgels made of poly(N-isopropylacrylamide) are the prototype of soft, thermoresponsive particles widely used to study fundamental problems in condensed matter physics. However, their internal structure is far from homogeneous, and existing mean-field approaches, such as Flory-Rehner theory, provide only qualitative descriptions of their thermoresponsive behavior. Here, we combine machine learning and numerical simulations to accurately predict the concentration and spatial distribution of crosslinkers, the latter hitherto unknown experimentally, as well as the full swelling behavior of microgels, using only polymer density profiles. Our approach provides unprecedented insight into structural and thermodynamic properties of any standard microgel, including experimental ones. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting structure and swelling of microgels with different crosslinker concentrations combining machine-learning with numerical simulations Marín-Aguilar, Susana Zaccarelli, Emanuela Soft Condensed Matter Microgels made of poly(N-isopropylacrylamide) are the prototype of soft, thermoresponsive particles widely used to study fundamental problems in condensed matter physics. However, their internal structure is far from homogeneous, and existing mean-field approaches, such as Flory-Rehner theory, provide only qualitative descriptions of their thermoresponsive behavior. Here, we combine machine learning and numerical simulations to accurately predict the concentration and spatial distribution of crosslinkers, the latter hitherto unknown experimentally, as well as the full swelling behavior of microgels, using only polymer density profiles. Our approach provides unprecedented insight into structural and thermodynamic properties of any standard microgel, including experimental ones. |
| title | Predicting structure and swelling of microgels with different crosslinker concentrations combining machine-learning with numerical simulations |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2502.07482 |