GRAINSMITH: A Generator of Polycrystalline Models for Atomistic Simulations with Statistical and Grain-Boundary Morphology Control

Abstract

Atomistic studies of grain-boundary engineering, grain-size effects and dopant enrichment require reproducible models with prescribed microstructural features. We present GRAINSMITH, an open-source Python package for generating statistically controlled polycrystalline models with selectable grain-boundary morphologies for molecular dynamics and subsequent relaxation. Within supported feature combinations, a single configuration specifies grain-size and volume distributions, crystallographic texture, boundary-area-weighted disorientation-angle distributions, phase composition and grain-boundary dopant placement. Crystal construction supports 230 crystallographic space groups. Periodic Voronoi and volume-targeted Laguerre tessellations provide planar boundaries, while distinct geometry backends generate smoothly curved and band-limited self-affine boundaries. A registry of twenty-six checks assesses applicable inputs, construction properties and outputs. Each run exports LAMMPS data and Extended XYZ files together with structural and statistical descriptors and machine-readable provenance. For a fixed software version and computational environment, the configuration and random seed determine byte-reproducible atomic configurations and scientific data across supported worker counts. By combining statistical specification, boundary-morphology control and reproducible atomistic output, GRAINSMITH supports systematic studies of microstructural effects and quantitative comparisons across generated models.