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].
