First example of multi-scale reverse Monte Carlo modeling for small-angle scattering experimental data using reverse mapping from coarse-grained particles to atoms

Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions

In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, I(q) vs q] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile.

Structural studies on fluid Hg and fluid Se at high temperatures and high pressures by means of X-ray diffraction and small angle X-ray scattering

X-ray diffraction (XRD) and small angle X-ray scattering (SAXS) measurements for fluid Hg and fluid Se up to the supercritical region have been carried out using synchrotron radiation at SPring-8. We obtained the structure factor, S ( Q ) $S\left(Q\right)$ , including a small angle region, and the pair distribution function, g ( r ) $g\left(r\right)$ , for both fluids from the liquid to the dense vapor region. Change of the local structure and medium-range correlations at the metal-insulator transition in fluid Hg were revealed. On the other, the average coordination number of two was preserved at the semiconductor-metal transition in fluid Se. From a SAXS experiment of fluid Se in 2012, SAXS spectra near the semiconductor-metal transition region show the Ornstein–Zernike profile and the SAXS intensity is reduced with increasing pressure. These results indicate difficulties of separating fluctuations intrinsic to the semiconductor-metal transition from those arising from the liquid-vapor critical point in fluid Se, although fluctuations intrinsic to the electronic transitions are largely expected in both fluids.