Ding Lijie, Chen Yihao, Do Changwoo
Neutron Scattering Division Oak Ridge National Laboratory,Oak Ridge TN37831 USA.
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA19104, USA.
J Appl Crystallogr. 2025 May 31;58(Pt 3):992-999. doi: 10.1107/S1600576725003280. eCollection 2025 Jun 1.
We have carried out theoretical analysis, Monte Carlo simulations and machine-learning analysis to quantify microscopic rearrangements of dilute dispersions of spherical colloidal particles from coherent scattering intensity. Both monodisperse and polydisperse dispersions of colloids were created and underwent a rearrangement consisting of an affine simple shear and non-affine rearrangement using the Monte Carlo method. We calculated the coherent scattering intensity of the dispersions and the correlation function of intensity before and after the rearrangement and generated a large data set of angular correlation functions for varying system parameters, including number density, polydispersity, shear strain and non-affine rearrangement. Singular value decomposition of the data set shows the feasibility of machine-learning inversion from the correlation function for the polydispersity, shear strain and non-affine rearrangement using only three parameters. A Gaussian process regressor is then trained on the data set and can retrieve the affine shear strain, non-affine rearrangement and polydispersity with relative errors of 3%, 1% and 6%, respectively. Altogether, our model provides a framework for quantitative studies of both steady and non-steady microscopic dynamics of colloidal dispersions using coherent scattering methods.
我们进行了理论分析、蒙特卡罗模拟和机器学习分析，以从相干散射强度量化球形胶体颗粒稀分散体的微观重排。创建了单分散和多分散的胶体分散体，并使用蒙特卡罗方法进行了由仿射简单剪切和非仿射重排组成的重排。我们计算了分散体的相干散射强度以及重排前后强度的相关函数，并针对包括数密度、多分散性、剪切应变和非仿射重排在内的不同系统参数生成了大量角相关函数数据集。数据集的奇异值分解表明，仅使用三个参数就可以从相关函数对多分散性、剪切应变和非仿射重排进行机器学习反演。然后在数据集上训练高斯过程回归器，它可以分别以3%、1%和6%的相对误差检索仿射剪切应变、非仿射重排和多分散性。总之，我们的模型为使用相干散射方法对胶体分散体的稳态和非稳态微观动力学进行定量研究提供了一个框架。
