• Bandgaps
• Electronic transport, effective mass, mobility
• Phonons, thermal transport
• Optical, magnetic, and mechanical properties
• Work functions
• Defect formation energy (neutral and charged)
• Phase diagrams
• Gas-surface processes for
  deposition and etching

• Periodic DFT with BAND and Quantum Espresso (QE)
• ReaxFF, Machine Learning Potentials
• Accurate functionals: metaGGA, hybrids, model potentials
• Non-equilibrium Green’s function (NEGF) DFT & DFTB
• Parametrized DFTB parameters for large scale simulations

• Automatic molecule-surface reaction discovery
• Molecule gun for vapor deposition and etching
• On-the-fly ML potential parametrization
• Large database of materials
• Crystal, surface, and tube builders
• Graphical user interface for QE and VASP
• Visualize band structures, phonons, DOS
• Field plot tool (planar averaged potential), work functions

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 in uence 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.