(362d) Computational Approach for Structure Generation of Anisotropic Particles (CASGAP) with Applications for Small Angle Scattering Analysis and Structural Color

In synthetic or biological soft materials, the presence of anisotropic building blocks can exhibit different extents of positional and orientational ordering that have a direct influence on their physical properties. Characterization of structural anisotropy is therefore critical in understanding the structure-property relationships and for designing materials with target properties. Towards this purpose, there is a need for computational generation of three-dimensional real space structures with desired structural features to aid analysis of an experimental characterization result like small angle scattering (SAS) profiles or to serve as initial configuration for physics-based simulation techniques that relate structure to properties (e.g., structural color [2], mechanical properties, etc.). Generation of structures with anisotropic particles/domains presents unique challenges as compared to their spherical counterpart. In this talk, we will highlight these challenges and present as a solution a new Computational Approach for Structure Generation of Anisotropic Particles (CASGAP) method [1] to generate a three-dimensional real space structure with anisotropic particles/domains with target distributions of particle/domain shapes, sizes, and orientational order. We will demonstrate the use of structures generated by CASGAP to analyze two-dimensional SAS profiles using another computational approach developed by the Jayaraman lab - CREASE (Computational Reverse Engineering Analysis for Scattering Experiments)[2-3]. We will also demonstrate the use of the structures generated from CASGAP in physics-based simulations to study structural evolution with time and to calculate properties of the structures.

[1] Nitant Gupta, Arthi Jayaraman, “Computational Approach for Structure Generation of Anisotropic Particles (CASGAP) with Targeted Distributions of Particle Design and Orientational Order” submitted for peer review in April 2023

[2]Christian M. Heil, Anvay Patil, Ali Dhinojwala, Arthi Jayaraman, Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions. ACS Cent. Sci. 2022, 8, 996– 1007

[3] Christian M. Heil, Yingzhen Ma, Bhuvnesh Bharti, and Arthi Jayaraman, Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (“P(q) and S(q) CREASE”) JACS Au 2023, 3, 3, 889–904