• Periodic DFT with BAND and Quantum ESPRESSO
• Machine learning potentials for materials: M3GNet, MACE, UMA, and external ASE models
• MACE and UMA foundation potentials for periodic materials workflows
• ReaxFF and DFTB for larger reactive and transport simulations
• NEGF with BAND and DFTB for electronic transport
• Simple Active Learning for training ML potentials on semiconductor-relevant configurations

• Band structures and effective masses
• Work functions from planar averaged potentials
• Defect formation-energy workflows, including charged defects
• Molecule gun and molecule sink for vapor deposition and etching
• Molecular dynamics for deposition, sputtering, thermal transport, and surface processes
• PES exploration for reaction pathways and transition states

Gain inside into the modeling concepts behind a fully atomistic simulation of chemical vapor deposition. A new step-by-step ReaxFF tutorial shows how acceleration methods combine with the molecule gun/sink to simulate chemical vapor / atomic layer deposition. The concepts discussed in the tutorial are in principle applicable to surface-atmosphere interactions, such as physical vapor deposition (PVD), solid-gas phase heterogeneous catalysis or plasma-surface reactions.

Point defects are omnipresent in materials and influence their electrical and optical properties. Understanding the formation of defects is critical for many industries in the area of physics and materials science. Therefore, first principles modeling of point defects has become an invaluable tool for understanding materials properties.

Methods and results

Defects can be easily created with the graphical user interface. Load a crystal, interactively delete, move, and create atoms. Define the calculation settings and compute the energy of the pristine and defective materials, as well as the chemical potential of the defect. For charged defects, the spurious long-range Coulomb interaction between supercell images can be corrected using ready-to-use Python workflows.