Computational Reverse Engineering of Scattering Experiments (CREASE) Method for Interpreting Structure in Soft Materials

In this talk, we will describe ‘Computational Reverse Engineering of Scattering Experiments (CREASE)’, a computational method that we have developed for analysis of small angle scattering profiles and interpretation of the structure in soft materials. CREASE is useful to interpret structural detail at a range of length scales for soft materials without relying on fitting small angle scattering profiles with off-the-shelf analytical models that may be too approximate for novel polymers and/or unconventional assembled structures. We will share multiple examples of how we have applied CREASE to experimental small angle X-ray scattering (SAXS) and neutron scattering (SANS) profiles obtained from different classes of soft materials [e.g., methylcellulose fibrillar structures (<i>Macromolecules </i>2022, 55, 24, 11076–11091), micelles in amphiphilic polymer solutions [<i>ACS Polymers Au</i>, 2021, 1, 3, 153–164], and segregation in binary nanoparticle mixtures [<i>JACS Au</i>, 2023, 3, 3, 889–904 and <i>ACS Central Science,</i> 2022, 8, 7, 996–1007]. Through these examples we will show how CREASE can be used to test various hypotheses regarding the assembled domain shapes and sizes within the materials’ structure and identify the relevant structural dimensions. Besides identifying relevant structural dimensions, the structures’ representative 3D configurations that CREASE outputs can also serve as an input for other computational methods that predict macroscopic properties (e.g., color, reflectance profiles) thus serving as a valuable tool for predicting structure-property relationships [e.g., <i>Science Advances</i> 2023, 9, 21, eadf2859; <i>ACS Materials Letters</i> 2022, 4, 9, 1848–1854] Machine learning enhanced CREASE also enables fast automated analyses of high throughput SAXS or SANS data facilitating future automation in soft materials structural characterization. Interested readers can find more information about CREASE on these two links:<br/>https://crease-ga.readthedocs.io/en/latest/ and https://github.com/arthijayaraman-lab/crease_ga .