Li Wenhui, Kadupitiya Jcs, Jadhao Vikram
Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA.
Polymers (Basel). 2023 May 2;15(9):2166. doi: 10.3390/polym15092166.
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.
从小分子液体和聚合物的流变特性在分子尺度上进行理解,对于优化它们在润滑和水力压裂等实际应用中的性能至关重要。我们将非平衡分子动力学模拟与两种无监督机器学习方法相结合:主成分分析(PCA)和t分布随机邻域嵌入(t-SNE),以提取在293K下,压力P∈0.1 - 955MPa时,以高应变率(10⁶ - 10¹⁰s⁻¹)剪切的角鲨烷的流变特性与分子结构之间的相关性,在此条件下观察到显著的剪切变稀现象。使用PCA和t-SNE对435×6维的分子内原子对取向张量和61×6维的分子间原子对取向张量进行降维和可视化,以评估角鲨烷剪切变稀过程中取向序的变化。在低压P = 0.1、100MPa下对分子内取向张量进行降维,揭示了应变率变化与角鲨烷分子相关的侧链-主链原子对、端链-主链原子对、短主链-主链原子对和长主链-主链原子对的取向之间存在很强的相关性。在高压P≥400MPa时,这些不同的原子对类型而不是应变率能更好地对取向张量进行分类,这表明分子内取向随应变率变化的整体演变有限。降维还没有发现高压下剪切变稀与分子间取向变化之间存在明确联系的证据。发现在高压下角鲨烷表现出显著剪切变稀的整个速率范围内,角鲨烷分子的排列已达到饱和。