Modeling polycrystalline structures using asymmetric gaussian grain distribution

Abstract

• A method to generate polycrystals using asymmetric Gaussian grain distribution. • Enables control over grain size, spatial distribution, and phase behavior. • Realistically reflects grain anisotropy and heterogeneity in materials. • Supports investigation of microstructure–property relationships. This work presents a computational method to generate polycrystalline structures based on an asymmetric Gaussian grain distribution, designed to reproduce the anisotropy and heterogeneity commonly observed in experime materials. Current modeling approaches often fail to capture the realistic spatial distribution of grains, limiting predictive insights into structure property relationships. The proposed Python-based tool addresses this gap by allowing fine control over grain size, spatial placement, and phase compatibility, making it applicable to both miscible and phase-separated systems. The method produces structures exported in standard formats for post-processing, visualization, and microstructure analysis. Its adaptability across material systems enables the study of how grain morphology and arrangement affect physical and mechanical behavior. This framework offers a practical step toward more realistic digital twins of polycrystalline materials, with direct implications for materials design and performance optimization.