Native Defect Landscape
Enumerate vacancies, interstitials, antisites, and relevant complexes; select supercells and charge states; calculate formation energies, transition levels, and chemical-potential limits with finite-size corrections.
Defect and Dopant Optimization for Semiconductor Materials
Point defects and dopants can set carrier type and concentration, compensation behavior, trap depth, non-radiative loss, ionic transport, and long-term device stability. CD ComputaBio integrates defect thermodynamics, electronic-structure calculations, configurational search, and uncertainty-aware ranking to turn these coupled variables into testable materials decisions.
Projects can begin from a crystal structure, a shortlist of dopants, measured conductivity or spectroscopy, synthesis conditions, or a broader design question. Within our AI for Semiconductor and Electronic Materials framework, we define the relevant defect chemistry and process window before selecting the minimum sufficient computational route.
Scope discipline: equilibrium defect concentrations are reported for explicit chemical-potential and temperature assumptions. Metastable structures, implantation damage, interfaces, grain boundaries, and non-equilibrium processing are treated separately when relevant.
Core Services
Dopant and Co-Dopant Screening
Rank substitutional and interstitial dopants by site preference, incorporation energy, ionization depth, solubility proxies, compensation risk, structural distortion, and target carrier response.
Defect Complexes and Passivation
Evaluate defect–dopant and defect–hydrogen binding, charge-state-dependent association, passivation pathways, and possible reactivation under annealing, illumination, or bias.
Carrier and Dopability Windows
Solve charge neutrality across temperature and chemical potentials to estimate self-consistent Fermi levels, defect/carrier populations, compensation boundaries, and achievable n- or p-type regimes.
Trap and Recombination Risk
Identify deep states and localized orbitals using appropriate functionals; prioritize candidates for configuration-coordinate, carrier-capture, or spectroscopy-focused follow-up rather than inferring lifetime from level position alone.
AI-Assisted Search and Ranking
Use descriptors, surrogate models, active learning, and chemically informed structure generation to triage large spaces. Cross-validation, domain checks, uncertainty, and physics-based recalculation are included in the ranking logic.
Integrated Project Workflow
| Stage | Key Activities | Decision Output |
|---|---|---|
| 1. Scope and Acceptance Criteria | Define host phases, target carrier type, operating environment, synthesis/annealing conditions, excluded chemistries, and experimental benchmarks. | Project specification, success metrics, and validation plan. |
| 2. Data and Structure Curation | Check stoichiometry, symmetry, magnetic order, polymorph stability, band-gap treatment, measured inputs, and provenance. | Versioned host structures and calculation-ready data package. |
| 3. Defect Space Construction | Generate native defects, dopants, charge states, complexes, and symmetry-inequivalent sites; design convergence-tested supercells. | Traceable defect catalogue and compute plan. |
| 4. High-Throughput Triage | Apply lower-cost DFT and/or validated surrogate models; flag relaxations, electronic states, and candidates outside the model domain. | Prioritized candidate set with uncertainty and failure flags. |
| 5. Refined Physics | Re-relax low-energy configurations; use band-gap-appropriate methods, charge corrections, chemical potentials, and finite-temperature defect thermodynamics. | Formation-energy diagrams, transition levels, and dopability windows. |
| 6. Decision and Experimental Handoff | Rank candidates against incorporation, compensation, trap, stability, cost, and process constraints; map predictions to feasible measurements. | Shortlist, risk register, and targeted synthesis/characterization recommendations. |
Decision-Ready Deliverables
- Curated host, defect, dopant, and complex structures with provenance
- Charge-state and site-selection rationale with convergence records
- Formation-energy and charge-transition-level diagrams
- Chemical-potential stability regions and competing-phase constraints
- Self-consistent Fermi-level, carrier, and defect concentration maps
- Dopant incorporation, compensation, and passivation ranking
- Electronic-state, localization, and trap-risk analysis
- Machine-readable tables, calculation metadata, and technical report
- Uncertainty, applicability-domain, and sensitivity assessment
- Experimental matrix for growth, annealing, spectroscopy, and transport
Applications
Power and RF Semiconductors
Assess compensating defects, deep traps, and dopant activation in wide- and ultrawide-band-gap hosts.
Photovoltaics and Detectors
Prioritize defect suppression and passivation strategies that reduce recombination and stabilize carrier collection.
Transparent Conductors
Balance carrier generation, optical transparency, phase stability, and native-defect compensation.
LED and Laser Materials
Investigate dopant activation, color-center formation, non-radiative traps, and growth-window sensitivity.
Quantum Defects
Screen charge-state stability, local symmetry, spin-compatible configurations, and competing non-target defects.
Oxides, Halides, and 2D Materials
Adapt electrostatic, dielectric, finite-size, and environmental treatments to the host dimensionality and bonding.
Scientific Evidence and Open-Access Context
Modern defect workflows require more than a single relaxed vacancy calculation. Robust studies combine systematic structure generation, charged-supercell corrections, chemical-potential limits, transition levels, self-consistent carrier statistics, and explicit searches for symmetry-broken or metastable configurations.1,2
References
- Kavanagh, S. R.; Walsh, A.; Scanlon, D. O. doped: Python toolkit for robust and repeatable charged defect supercell calculations. Journal of Open Source Software 2024, 9(96), 6433. https://doi.org/10.21105/joss.06433. (Open Access; CC BY 4.0)
- Mosquera-Lois, I.; Kavanagh, S. R.; Walsh, A.; Scanlon, D. O. ShakeNBreak: Navigating the defect configurational landscape. Journal of Open Source Software 2022, 7(80), 4817. https://doi.org/10.21105/joss.04817. (Open Access; CC BY 4.0)
Why Work with CD ComputaBio?
Our projects are organized around the experimental decision—not a fixed software stack. We connect atomic structures, correction choices, chemical environments, process assumptions, model uncertainty, and candidate ranking in a traceable workflow. High-value predictions are paired with practical validation routes such as temperature-dependent Hall measurements, DLTS, EPR, PL, XPS, SIMS, positron annihilation, or controlled annealing studies as appropriate.
To discuss your host material, dopant space, defect hypothesis, available characterization data, or fabrication constraints, Contact Us or submit the Online Inquiry below.
