Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Li, Haoxiang; Tian, Hao; Chen, Yewei; Xiao, Sian; Zhao, Xiuying; Gao, Yangyang; Zhang, Liqun

doi:10.1021/acs.jpcb.3c01697

Cited by 5 publications

(2 citation statements)

References 71 publications

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“…We note that following our earlier work [24], similar efforts combining NEMD simulations and ML in the area of rheology have appeared in the literature [27,28]. In particular, in Ref.…”

Section: Introductionmentioning

confidence: 54%

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Kadupitiya

Jadhao

2023

Polymers

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Molecular-scale understanding of rheological properties of small-molecular liquids and polymers is critical to optimizing their performance in practical applications such as lubrication and hydraulic fracking. We combine nonequilibrium molecular dynamics simulations with two unsupervised machine learning methods: principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), to extract the correlation between the rheological properties and molecular structure of squalane sheared at high strain rates (106–1010s−1) for which substantial shear thinning is observed under pressures P∈0.1–955 MPa at 293 K. Intramolecular atom pair orientation tensors of 435×6 dimensions and the intermolecular atom pair orientation tensors of 61×6 dimensions are reduced and visualized using PCA and t-SNE to assess the changes in the orientation order during the shear thinning of squalane. Dimension reduction of intramolecular orientation tensors at low pressures P=0.1,100 MPa reveals a strong correlation between changes in strain rate and the orientation of the side-backbone atom pairs, end-backbone atom pairs, short backbone-backbone atom pairs, and long backbone-backbone atom pairs associated with a squalane molecule. At high pressures P≥400 MPa, the orientation tensors are better classified by these different pair types rather than strain rate, signaling an overall limited evolution of intramolecular orientation with changes in strain rate. Dimension reduction also finds no clear evidence of the link between shear thinning at high pressures and changes in the intermolecular orientation. The alignment of squalane molecules is found to be saturated over the entire range of rates during which squalane exhibits substantial shear thinning at high pressures.

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“…We note that following our earlier work [24], similar efforts combining NEMD simulations and ML in the area of rheology have appeared in the literature [27,28]. In particular, in Ref.…”

Section: Introductionmentioning

confidence: 54%

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Kadupitiya

Jadhao

2023

Polymers

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“…Predicting material properties from their structures is a fundamental goal in materials science research as it is crucial for discovering and designing new materials with desired properties. In recent years, computational chemistry techniques such as molecular simulations and density functional theory have made significant progress in achieving this goal. − However, high-precision calculations are often computationally expensive and time-consuming, requiring sophisticated models and a high-performance computing infrastructure. The emergence of machine learning (ML) methods has provided a promising approach for fast and accurate prediction of material properties, sustained by an explosion in data from various scientific domains and the advancement of computing power. − …”

Section: Introductionmentioning

confidence: 99%

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

Deng,

Chen,

et al. 2023

J. Phys. Chem. B

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To discover new materials, high-throughput screening (HTS) with machine learning (ML) requires universally available descriptors that can accurately predict the desired properties. For elastomers, experimental and simulation data in current descriptors may not be available for all candidates of interest, hindering elastomer discovery through HTS. To address this challenge, we introduce structure-based multilevel (SM) descriptors of elastomers derived solely from molecular structure that is universally available. Our SM descriptors are hierarchically organized to capture both local soft and hard segment structures as well as the global structures of elastomers. With the SM-Morgan Fingerprint (SM-MF) descriptor, one of our SM descriptors, a machine learning model accurately predicts elastomer toughness with a remarkable accuracy of 0.91. Furthermore, an HTS pipeline is established to swiftly screen elastomers with targeted toughness. We also demonstrate the generality and applicability of SM descriptors by using them to construct HTS pipelines for screening elastomers with a targeted critical strain or Young's modulus. The user-friendliness and low computational cost of SM descriptors make them a promising tool to significantly enhance HTS in the search for novel materials.

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Machine learning‐driven optimization and prediction of poly(lactic acid)/poly(butylene‐adipate‐co‐terephthalate)/clay blend nanocomposites in structure–property relationships: A combination of experimental and computational techniques

Akbarzadeh,

Gorji,

Tayouri

et al. 2024

Polymer Engineering & Sci

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The melt‐mixing method in a twin screw extruder was utilized to develop blended nanocomposites of poly(lactic acid: PLA) and poly(butylene‐adipate‐co‐terephthalate: PBAT) with three combinations of weight percent 90/10, 70/30, and 50/50 (w/w) which contain 1, 3, and 5 phr of organic clays (Cloisite 30B). As a result, both x‐ray diffraction analysis and scanning electron microscopy revealed a consistent compatibilization effect of the nanoclays (NCs) within the PLA/PBAT matrix. The interfacial adhesion between the blend components is improved in matrix droplet structures, where the PBAT domains are evenly dispersed owing to the uniform dispersion of NCs at optimal 3 phr. Furthermore, NCs at this concentration acted as nucleating agents in the PLA/PBAT blends and enhanced the crystallization rate of the blend nanocomposites by lowering the cold crystallization temperature of the 90/10 blend by 5°C, as determined by differential scanning calorimetry (DSC). The neat PLA/PBAT blends displayed a drop in tensile strength and modulus, but their properties were restored when NCs were interfacially localized. In addition, elongation at the break point increased from ∼5.5% (70/30) to ∼5.9% (70/30/1). A comparison was also made between the theoretical values of the tensile modulus calculated using three‐phase mathematical models and the experimental results. According to the Carreau‐Yasuda model, the viscosity of blend nanocomposites increases with the addition of NC. The percolation threshold between the two filler contents was also elucidated as the storage modulus increased significantly from 70/30 (∼28 Pa) to 70/30/3 (∼2000 Pa) at low frequency. Furthermore, machine learning (ML) has been demonstrated to be an effective tool for data‐driven multi‐physical modeling that can be exploited to optimize rheological properties. The extreme gradient boosting (XGBoost) model yields the most accurate viscosity (η) prediction. Finally, the thermal stability of the blended components was examined via thermogravimetry analysis (TGA) and was found to be improved with the use of NCs.Highlights Structure–property models were developed by experimental and computational data. Young's modulus was predicted with high accuracy by Paul and Halpin‐Tsai models. XGBoost modeled the rheological data well based on the machine learning technique.

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Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning

Cited by 5 publications

References 71 publications

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates

Structure-Based Multilevel Descriptors for High-throughput Screening of Elastomers

Machine learning‐driven optimization and prediction of poly(lactic acid)/poly(butylene‐adipate‐co‐terephthalate)/clay blend nanocomposites in structure–property relationships: A combination of experimental and computational techniques

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