Mat Defect Energy Dft

Point-Defect Formation Energy (DFT)

Goal

To calculate the formation energy of point defects (vacancies, substitutions, interstitials) including charged defect states and finite-size corrections using DFT (VASP) via atomate2 workflows. This produces formation energy diagrams showing defect charge transition levels as a function of Fermi energy.

$$E_f[D^q] = E[D^q] - E[\text{bulk}] + \sum_i \Delta n_i \mu_i + q(E_\text{VBM} + \Delta E_F) + E_\text{corr}$$

where $q$ is the charge state, $E_\text{VBM}$ is the valence band maximum, $\Delta E_F$ is the Fermi energy relative to VBM, and $E_\text{corr}$ is the finite-size correction (Freysoldt/FNV).

Instructions

1. Obtain Bulk Structure

Start with a relaxed primitive cell:

mcp_base_search_materials_project_by_formula(formula="MgO", save_to_file="MgO.cif")

2. Generate Defect Structures

Use pymatgen-analysis-defects to generate all symmetry-unique defect supercells with charge states:

# Env: base-agent
python .agents/skills/mat-defect-energy-dft/scripts/generate_defect_structures.py \
    --bulk MgO.cif \
    --supercell_size 3 3 3 \
    --defect_type vacancy \
    --charge_range -2 2 \
    --output dft_defects/

This generates:

• POSCAR files for each defect × charge state
• A defect_index.json mapping defect names to charge states and structures
• Pristine supercell for the bulk reference

3. Run DFT Calculations (atomate2)

Submit calculations via the atomate2 MCP tool:

# Bulk supercell reference
mcp_atomate2_run_atomate2_vasp_calculation(
    structures_path="dft_defects/pristine_supercell.cif",
    output_dir="./dft_bulk/",
    calculation_type="static",
    preset_type="matpes-pbe",
    execution_mode="remote"
)

# All defect structures
mcp_atomate2_run_atomate2_vasp_calculation(
    structures_path="dft_defects/",
    output_dir="./dft_defect_calcs/",
    calculation_type="relaxation",
    preset_type="matpes-pbe",
    execution_mode="remote"
)

Alternative: atomate2 FormationEnergyMaker

For fully automated defect workflows with built-in corrections:

# Env: atomate2-agent (Python API)
from atomate2.vasp.flows.defect import FormationEnergyMaker
from pymatgen.analysis.defects.generators import VacancyGenerator
from pymatgen.core import Structure

bulk = Structure.from_file("MgO.cif")
vac_gen = VacancyGenerator()
defects = vac_gen.generate(bulk)

maker = FormationEnergyMaker()
# Submit via jobflow-remote for each defect

4. Parse Results and Compute Formation Energies

After DFT calculations complete:

# Env: base-agent
python .agents/skills/mat-defect-energy-dft/scripts/parse_defect_results.py \
    --bulk_dir dft_bulk/ \
    --defect_dir dft_defect_calcs/ \
    --defect_index dft_defects/defect_index.json \
    --dielectric 9.8 \
    --output formation_energies.json \
    --plot formation_energy_diagram.png

The script:

• Parses VASP outputs for total energies
• Applies Freysoldt (FNV) finite-size corrections for charged defects
• Determines VBM and band gap from bulk calculation
• Constructs the formation energy diagram

5. Interpret Results

The formation energy diagram shows:

• Slopes: Each line segment has slope = charge state $q$
• Transition levels: Intersections where the stable charge state changes ($\epsilon(q/q')$)
• Low formation energy → high concentration: Defects with low $E_f$ at the Fermi level are most abundant

Examples

Oxygen Vacancy in MgO

# 1. Get MgO
mcp_base_search_materials_project_by_formula(formula="MgO")

# 2. Generate defects with charges -2 to +2
python .agents/skills/mat-defect-energy-dft/scripts/generate_defect_structures.py \
    --bulk MgO.cif --supercell_size 3 3 3 --defect_type vacancy --charge_range -2 2 --output mgo_defects/

# 3. Run DFT (remote)
mcp_atomate2_run_atomate2_vasp_calculation(
    structures_path="mgo_defects/", output_dir="./mgo_dft/",
    calculation_type="relaxation", preset_type="matpes-pbe", execution_mode="remote"
)

# 4. Parse and plot (after DFT completes)
python .agents/skills/mat-defect-energy-dft/scripts/parse_defect_results.py \
    --bulk_dir mgo_dft/pristine_supercell/ --defect_dir mgo_dft/ \
    --defect_index mgo_defects/defect_index.json --dielectric 9.8 --output mgo_fe.json

Constraints

• VASP required: Actual DFT calculations require a valid VASP setup (PMG_VASP_PSP_DIR, atomate2/jobflow-remote configured).
• Supercell size: Use at least 3×3×3 for cubic systems. Charged defect corrections are less reliable for small cells.
• Dielectric constant: The Freysoldt correction requires the static dielectric constant of the host material. Use experimental or computed values.
• Functional: PBE underestimates band gaps → transition levels may be shifted. For accurate results, use HSE06 hybrid functional (requires custom INCAR settings).
• Environments:
  • Structure generation: base-agent (pymatgen-analysis-defects)
  • DFT submission: atomate2-agent (via MCP tool)
  • Post-processing: base-agent
• For neutral defects only (no DFT): See mat-defect-energy for MLIP-based approach.

Author: Bowen Deng Contact: GitHub @learningmatter-mit