FAIR Data Management of the Results from the MulSKIPS Atomistic Simulation Environment for PVD, CVD, and Laser Annealing

This thesis presents the design and implementation of a FAIR (Findable, Accessible, Interoperable, Reusable) data management workflow for atomistic simulations of 3C-SiC growth via Physical Vapor Deposition (PVD), using the MulSKIPS multiscale Kinetic Monte Carlo framework [1–3]. The simulation engine is capable of capturing extended defect formation—including stacking faults (SFs) and antiphase boundaries (APBs)—with atomistic resolution under experimentally relevant conditions [3, 4]. A central achievement of this work is the development of a Python-based parser that automates the extraction of simulation metadata and results, generating NeXus files that conform to FAIRmat’s contributed definitions NXmicrostructure_imm_config and NXmicrostructure_imm_results [5]. This enables machineactionable, semantically rich data outputs that are compatible with the NOMAD repository [6] and the European Open Science Cloud (EOSC) ecosystem [7]. The simulation–data integration pipeline was validated on PVD simulations of 3C-SiC substrates, demonstrating reproducibility, robust metadata curation, and automated defect quantification. While the implementation was tested on PVD only, the modular architecture of the workflow is readily extensible to Chemical Vapor Deposition (CVD) and Pulsed Laser Annealing (PLA) simulations, supporting the future development of interoperable digital twins for materials processing [4, 8].