FAIR Data Management of the Results from the MulSKIPS Atomistic Simulation Environment for Physical Vapor Deposition

This work presents the development of a FAIR (Findable, Accessible, Interoperable, Reusable) data management workflow for atomistic simulations of 3C-SiC growth via Physical Vapor Deposition (PVD), using the multiscale Kinetic Monte Carlo environment MulSKIPS. The simulation engine models extended defect formation—such as stacking faults and antiphase boundaries—with atomistic resolution under experimentally relevant conditions. A core contribution is the implementation of a Python-based parser that extracts simulation metadata and results to produce NeXus files conforming to the FAIRmat-contributed definitions NXmicrostructure_imm_config and NXmicrostructure_imm_results. The resulting data outputs are semantically structured, machine-actionable, and fully compatible with the NOMAD repository. The integrated simulation and data curation pipeline was validated through simulations of 3C-SiC substrates, demonstrating reproducibility, metadata robustness, and automated defect quantification. Although focused on PVD, the workflow is modular and extensible to Chemical Vapor Deposition (CVD) and Pulsed Laser Annealing (PLA), paving the way for future digital twin frameworks in materials processing.