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atomode

ATOMic Order-to-Disorder Engine

Generate atomistic supercells across the full order-to-disorder spectrum (amorphous, short- and medium-range order, nanocrystalline, and single crystal) at any size, by seeding Voronoi grains and refining them into physically realistic structures with a machine-learning potential (MACE-MP0) or a fast spring-network quench.

Silicon, liquid → nanocrystalline. See Static Examples → Silicon.

Overview

atomode builds periodic supercells across the order-to-disorder spectrum by seeding Voronoi grains and refining them into physically realistic structures, either with a fast spring-network (FIRE) quench or with the MACE-MP0 machine-learning potential for near-DFT accuracy. Grain size and density set the local order, from a single amorphous network up to large, well-aligned crystallites. Pair (g2) and rooted three-body (g3) distributions are provided to characterize the result.

Key features:

Refining generated structures

atomode generates the initial supercell; a separate relaxation step turns it into a physically realistic structure. Two worked pipelines are documented, in order of accuracy: