Hagita Katsumi, Teramoto Takashi
Department of Applied Physics, National Defense Academy, Yokosuka 239-8686, Japan.
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 May;77(5 Pt 2):056704. doi: 10.1103/PhysRevE.77.056704. Epub 2008 May 15.
A combination of reverse Monte Carlo (RMC) and computational homology is examined as a useful approach in connecting scattering experiments to mathematics for 3D morphology modeling. We develop a different method of morphology modeling from multiple two-dimensional (2D) scattering patterns of structure functions by RMC technique using coarse-grained particles. We perform RMC analysis for multiple 2D scattering patterns of the configuration generated from the surface equation of double gyroid morphology. Homology analysis enables us to classify complex three-dimensional morphologies by incorporating topologically invariant quantities, the so-called Betti numbers. It is demonstrated that RMC analysis reconstructs the DG morphology from multiple 2D scattering patterns.
本文研究了将反向蒙特卡罗（RMC）和计算同源性相结合，作为一种将散射实验与数学联系起来以进行三维形态建模的有效方法。我们通过使用粗粒度粒子的RMC技术，从结构函数的多个二维（2D）散射模式中开发了一种不同的形态建模方法。我们对由双曲面形态的表面方程生成的构型的多个二维散射模式进行RMC分析。同源性分析使我们能够通过纳入拓扑不变量，即所谓的贝蒂数，对复杂的三维形态进行分类。结果表明，RMC分析能够从多个二维散射模式中重建双曲面形态。
