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) CREASEâ€) JACS Au 2023, 3, 3, 889–904
