Probing Rate-Dependent Liquid Shear Viscosity Using Combined Machine Learning and Non-Equilibrium Molecular Dynamics
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917968017883136 |
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| author | Gao, Hongyu Zhu, Minghe Ma, Jia Honecker, Marc Li, Kexian |
| author_facet | Gao, Hongyu Zhu, Minghe Ma, Jia Honecker, Marc Li, Kexian |
| contents | Accurately measuring liquid dynamic viscosity across a wide range of shear rates, from the linear-response to shear-thinning regimes, presents significant experimental challenges due to limitations in resolving high shear rates and controlling thermal effects. In this study, we integrated machine learning (ML) with non-equilibrium molecular dynamics (NEMD) simulations to address these challenges. A supervised artificial neural network (ANN) model was developed to predict viscosity as a function of shear rate, normal pressure, and temperature, effectively capturing the complex interplay among these variables. The model reveals distinct trends in shear viscosity, characterized by the shear-thinning exponent, and highlights non-monotonic behavior in the radius of gyration components, reflecting molecular morphological changes driven by rate-dependent volume expansion. Notably, temperature effects diminish at higher shear rates, where molecular alignment and spacing dominate the response to shear. By implementing the 'fix npt/sllod' command in LAMMPS, we achieve precise constant-pressure control in NEMD simulations, ensuring accurate representation of system dynamics. This study demonstrates the potential of ML-enhanced NEMD for efficient and accurate viscosity prediction, providing a robust framework for future research in complex fluid dynamics and material design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19805 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probing Rate-Dependent Liquid Shear Viscosity Using Combined Machine Learning and Non-Equilibrium Molecular Dynamics Gao, Hongyu Zhu, Minghe Ma, Jia Honecker, Marc Li, Kexian Materials Science Accurately measuring liquid dynamic viscosity across a wide range of shear rates, from the linear-response to shear-thinning regimes, presents significant experimental challenges due to limitations in resolving high shear rates and controlling thermal effects. In this study, we integrated machine learning (ML) with non-equilibrium molecular dynamics (NEMD) simulations to address these challenges. A supervised artificial neural network (ANN) model was developed to predict viscosity as a function of shear rate, normal pressure, and temperature, effectively capturing the complex interplay among these variables. The model reveals distinct trends in shear viscosity, characterized by the shear-thinning exponent, and highlights non-monotonic behavior in the radius of gyration components, reflecting molecular morphological changes driven by rate-dependent volume expansion. Notably, temperature effects diminish at higher shear rates, where molecular alignment and spacing dominate the response to shear. By implementing the 'fix npt/sllod' command in LAMMPS, we achieve precise constant-pressure control in NEMD simulations, ensuring accurate representation of system dynamics. This study demonstrates the potential of ML-enhanced NEMD for efficient and accurate viscosity prediction, providing a robust framework for future research in complex fluid dynamics and material design. |
| title | Probing Rate-Dependent Liquid Shear Viscosity Using Combined Machine Learning and Non-Equilibrium Molecular Dynamics |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.19805 |