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chem-dft-orca-optimization

Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.

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Precio sin confirmar★ 161 Estrellas de GitHubRegistro actualizado · 6 sept 2026agent-skill

Resumen

Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

DFT Geometry Optimization with ORCA

Goal

Optimize the geometry of a molecular structure at the DFT level using the ORCA quantum chemistry program. Supports two modes: minimization (finding the nearest local minimum) and transition state (TS) optimization (single-ended saddle point search). The calculation uses the SCINE/ReaDuct wrapper for robust optimizer management.

[!IMPORTANT] This skill provides single-ended TS optimization only. For reaction pathway methods (NEB, IRC), consider using the MLIP-based NEB skill or IRC skill with MLIP pre-screening, then refine with DFT. For advanced ORCA features, use the advanced ORCA skill.

Background

Geometry optimization iteratively adjusts nuclear positions to minimize (or, for TS search, to find a first-order saddle point of) the potential energy surface $E(\mathbf{R})$. The SCINE/ReaDuct optimizer handles step control, coordinate transformations, and convergence criteria internally.

  • Minimization seeks a stationary point where $\nabla E = 0$ and the Hessian has all positive eigenvalues.
  • TS optimization seeks a first-order saddle point where $\nabla E = 0$ and the Hessian has exactly one negative eigenvalue.

1. Prerequisites

  • Conda environment: orca-agent with scine_utilities, scine_readuct, and ase installed
  • ORCA binary: The environment variable ORCA_BINARY_PATH must point to the ORCA executable 
    export ORCA_BINARY_PATH=/path/to/orca
  • Input structure: A molecular structure file readable by ASE (.xyz, .cif, .mol, etc.)
  • For TS optimization: Provide a reasonable TS guess geometry. Poor initial guesses will likely fail to converge to the correct saddle point.

2. Parameters

ParameterDefaultDescription
--structure(required)Path to input structure file
--opt_typeminmin for minimization, ts for transition state search
--charge0Molecular charge
--spin_multiplicity1Spin multiplicity (2S+1)
--functionalPBEDFT functional (e.g. PBE, B3LYP, wB97X-V)
--basis_setdef2-SVPBasis set (e.g. def2-SVP, def2-TZVP)
--dispersionNoneDispersion correction (e.g. D3BJ, D4)
--solvationNoneImplicit solvation model: CPCM or SMD
--solventNoneSolvent name; required if --solvation is set
--special_optionNOSOSCFORCA special option passed to SCINE calculator. Set to empty string to disable.
--nprocs1Number of CPU cores for ORCA
--convergence_max_iterations200Maximum optimization steps
--calculate_final_hessianoffCompute Hessian at optimized geometry (for TS verification)
--calculator_settingsNoneExtra SCINE calculator settings as a JSON string (see below)
--optimizer_settingsNoneExtra ReaDuct optimizer kwargs as a JSON string (see below)
--output_dirautoOutput directory

3. Running an Optimization

Geometry minimization
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --dispersion D3BJ \
    --nprocs 4 \
    --output_dir research/my_project/optimization
Transition state optimization
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure ts_guess.xyz \
    --opt_type ts \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --dispersion D3BJ \
    --calculate_final_hessian \
    --nprocs 4 \
    --output_dir research/my_project/ts_optimization
With extra settings (calculator + optimizer)

For settings not exposed as dedicated flags, pass JSON strings. --calculator_settings applies to the SCINE/ORCA calculator, --optimizer_settings applies to the ReaDuct optimization task. SCINE is strict about types, so JSON ensures values are passed with the correct type (int, float, string).

# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --calculator_settings '{"max_scf_iterations": 128}' \
    --optimizer_settings '{"convergence_delta_value": 1e-6}' \
    --output_dir research/my_project/opt_custom
With implicit solvation
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional PBE0 \
    --basis_set def2-TZVP \
    --solvation SMD \
    --solvent water \
    --nprocs 4 \
    --output_dir research/my_project/opt_solvated

4. Output Files

  • optimization_results.json: Structured results containing:
    • converged: Boolean indicating whether the optimization converged
    • final_energy_hartree, final_energy_eV: Final electronic energy
    • final_max_force_eV_per_Ang, final_rms_force_eV_per_Ang: Residual force information
    • opt_type: Whether this was a minimization or TS search
    • If --calculate_final_hessian was used: hessian_eV_per_Ang2, hessian_wave_numbers_cm-1, and n_imaginary_modes
    • All input parameters for reproducibility
  • initial_structure.xyz: Copy of the input structure
  • optimized_structure.xyz: The optimized geometry

5. Interpreting Results

Minimization
  • Check converged: true in the results JSON.
  • Residual forces should be small (max force < 0.01 eV/A for typical convergence).
  • If convergence fails, try increasing --convergence_max_iterations or improving the initial geometry.
TS Optimization
  • Convergence alone does not guarantee a valid TS. After convergence, verify the Hessian has exactly one imaginary frequency:
    • Recommended: Use --calculate_final_hessian to compute the Hessian directly after optimization. The output will include n_imaginary_modes — expect exactly 1 for a valid TS.
    • Alternatively, run a separate single-point Hessian with the singlepoint skill using --compute_hessian.
  • Inspect the imaginary mode to confirm it corresponds to the expected reaction coordinate.
  • If the optimizer converges to a minimum instead of a saddle point, the initial guess was likely too far from the true TS.

6. Constraints

  • Non-periodic systems only: ORCA does not handle periodic boundary conditions.
  • Single-ended TS: Only single-ended TS optimization is available. For double-ended methods (NEB), pre-screen with MLIPs.
  • TS guess quality: The TS optimizer requires a reasonable initial guess. Generate one using constrained scans, interpolation, or MLIP-based TS search methods.
  • ORCA binary: ORCA_BINARY_PATH must be set and point to a working ORCA installation.
  • Environment: All commands require the orca-agent conda environment.
  • Solvation: When using --solvation, you must also provide --solvent.

References

  • Neese, F., "Software update: The ORCA program system—Version 5.0", WIREs Comput. Mol. Sci., 2022. DOI
  • Unsleber, J.P. et al., "SCINE—Software for Chemical Interaction Networks", J. Chem. Phys., 2024. DOI

Author: Miguel Steiner Contact: GitHub @steinmig

Ver fuente ↗
Metadatos del archivo
name: chem-dft-orca-optimization
description: Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
category: [chemistry]
Ver texto original
---
name: chem-dft-orca-optimization
description: Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
category: [chemistry]
---

# DFT Geometry Optimization with ORCA

## Goal

Optimize the geometry of a molecular structure at the DFT level using the ORCA quantum chemistry program. Supports two modes: **minimization** (finding the nearest local minimum) and **transition state (TS) optimization** (single-ended saddle point search). The calculation uses the SCINE/ReaDuct wrapper for robust optimizer management.

> [!IMPORTANT]
> This skill provides **single-ended TS optimization** only. For reaction pathway methods (NEB, IRC), consider using the MLIP-based [NEB skill](../chem-neb-barrier/SKILL.md) or [IRC skill](../chem-irc-verification/SKILL.md) with MLIP pre-screening, then refine with DFT. For advanced ORCA features, use the [advanced ORCA skill](../chem-dft-orca-advanced-calculation/SKILL.md).

## Background

Geometry optimization iteratively adjusts nuclear positions to minimize (or, for TS search, to find a first-order saddle point of) the potential energy surface $E(\mathbf{R})$. The SCINE/ReaDuct optimizer handles step control, coordinate transformations, and convergence criteria internally.

- **Minimization** seeks a stationary point where $\nabla E = 0$ and the Hessian has all positive eigenvalues.
- **TS optimization** seeks a first-order saddle point where $\nabla E = 0$ and the Hessian has exactly one negative eigenvalue.

## 1. Prerequisites

- **Conda environment:** `orca-agent` with `scine_utilities`, `scine_readuct`, and `ase` installed
- **ORCA binary:** The environment variable `ORCA_BINARY_PATH` must point to the ORCA executable
  ```bash
  export ORCA_BINARY_PATH=/path/to/orca
  ```
- **Input structure:** A molecular structure file readable by ASE (`.xyz`, `.cif`, `.mol`, etc.)
- For **TS optimization:** Provide a reasonable TS guess geometry. Poor initial guesses will likely fail to converge to the correct saddle point.

## 2. Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `--structure` | (required) | Path to input structure file |
| `--opt_type` | `min` | `min` for minimization, `ts` for transition state search |
| `--charge` | `0` | Molecular charge |
| `--spin_multiplicity` | `1` | Spin multiplicity (2S+1) |
| `--functional` | `PBE` | DFT functional (e.g. `PBE`, `B3LYP`, `wB97X-V`) |
| `--basis_set` | `def2-SVP` | Basis set (e.g. `def2-SVP`, `def2-TZVP`) |
| `--dispersion` | None | Dispersion correction (e.g. `D3BJ`, `D4`) |
| `--solvation` | None | Implicit solvation model: `CPCM` or `SMD` |
| `--solvent` | None | Solvent name; required if `--solvation` is set |
| `--special_option` | `NOSOSCF` | ORCA special option passed to SCINE calculator. Set to empty string to disable. |
| `--nprocs` | `1` | Number of CPU cores for ORCA |
| `--convergence_max_iterations` | `200` | Maximum optimization steps |
| `--calculate_final_hessian` | off | Compute Hessian at optimized geometry (for TS verification) |
| `--calculator_settings` | None | Extra SCINE calculator settings as a JSON string (see below) |
| `--optimizer_settings` | None | Extra ReaDuct optimizer kwargs as a JSON string (see below) |
| `--output_dir` | auto | Output directory |

## 3. Running an Optimization

### Geometry minimization

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --dispersion D3BJ \
    --nprocs 4 \
    --output_dir research/my_project/optimization
```

### Transition state optimization

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure ts_guess.xyz \
    --opt_type ts \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --dispersion D3BJ \
    --calculate_final_hessian \
    --nprocs 4 \
    --output_dir research/my_project/ts_optimization
```

### With extra settings (calculator + optimizer)

For settings not exposed as dedicated flags, pass JSON strings. `--calculator_settings` applies to the SCINE/ORCA calculator, `--optimizer_settings` applies to the ReaDuct optimization task. SCINE is strict about types, so JSON ensures values are passed with the correct type (int, float, string).

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional B3LYP \
    --basis_set def2-TZVP \
    --calculator_settings '{"max_scf_iterations": 128}' \
    --optimizer_settings '{"convergence_delta_value": 1e-6}' \
    --output_dir research/my_project/opt_custom
```

### With implicit solvation

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-optimization/scripts/run_optimization.py \
    --structure molecule.xyz \
    --functional PBE0 \
    --basis_set def2-TZVP \
    --solvation SMD \
    --solvent water \
    --nprocs 4 \
    --output_dir research/my_project/opt_solvated
```

## 4. Output Files

- `optimization_results.json`: Structured results containing:
  - `converged`: Boolean indicating whether the optimization converged
  - `final_energy_hartree`, `final_energy_eV`: Final electronic energy
  - `final_max_force_eV_per_Ang`, `final_rms_force_eV_per_Ang`: Residual force information
  - `opt_type`: Whether this was a minimization or TS search
  - If `--calculate_final_hessian` was used: `hessian_eV_per_Ang2`, `hessian_wave_numbers_cm-1`, and `n_imaginary_modes`
  - All input parameters for reproducibility
- `initial_structure.xyz`: Copy of the input structure
- `optimized_structure.xyz`: The optimized geometry

## 5. Interpreting Results

### Minimization
- Check `converged: true` in the results JSON.
- Residual forces should be small (max force < 0.01 eV/A for typical convergence).
- If convergence fails, try increasing `--convergence_max_iterations` or improving the initial geometry.

### TS Optimization
- Convergence alone does not guarantee a valid TS. After convergence, verify the Hessian has exactly one imaginary frequency:
  - **Recommended:** Use `--calculate_final_hessian` to compute the Hessian directly after optimization. The output will include `n_imaginary_modes` — expect exactly 1 for a valid TS.
  - Alternatively, run a separate single-point Hessian with the [singlepoint skill](../chem-dft-orca-singlepoint/SKILL.md) using `--compute_hessian`.
- Inspect the imaginary mode to confirm it corresponds to the expected reaction coordinate.
- If the optimizer converges to a minimum instead of a saddle point, the initial guess was likely too far from the true TS.

## 6. Constraints

- **Non-periodic systems only:** ORCA does not handle periodic boundary conditions.
- **Single-ended TS:** Only single-ended TS optimization is available. For double-ended methods (NEB), pre-screen with MLIPs.
- **TS guess quality:** The TS optimizer requires a reasonable initial guess. Generate one using constrained scans, interpolation, or MLIP-based TS search methods.
- **ORCA binary:** `ORCA_BINARY_PATH` must be set and point to a working ORCA installation.
- **Environment:** All commands require the `orca-agent` conda environment.
- **Solvation:** When using `--solvation`, you must also provide `--solvent`.

## References

- Neese, F., "Software update: The ORCA program system—Version 5.0", *WIREs Comput. Mol. Sci.*, 2022. [DOI](https://doi.org/10.1002/wcms.1606)
- Unsleber, J.P. et al., "SCINE—Software for Chemical Interaction Networks", *J. Chem. Phys.*, 2024. [DOI](https://doi.org/10.1063/5.0206974)

---

**Author:** Miguel Steiner
**Contact:** [GitHub @steinmig](https://github.com/steinmig)

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Repositorio fuente
learningmatter-mit/AtomisticSkills
Licencia
MIT
Versión
1.0.0
Último push de GitHub
3 sept 2026
Registro actualizado
6 sept 2026
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.agents/skills/chem-dft-orca-optimization/SKILL.md @ d1f7ecfda2cd

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    "score": 66,
    "label": "Manual review",
    "version": "trust-score-v4",
    "install_policy": "block",
    "evidence": {
      "stars": "161 GitHub stars",
      "repoActivity": "161 stars, 24 forks",
      "lastPushed": "1mo since push",
      "license": "MIT",
      "repository": "https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-dft-orca-optimization",
      "install": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
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      "agent-skill"
    ],
    "known_risks": [
      "The SKILL.md excerpt is truncated, but the provided content is comprehensive and well-structured.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 161 stars, 24 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
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  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 73,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "The SKILL.md excerpt is truncated, but the provided content is comprehensive and well-structured.",
      "The skill depends on external software (ORCA, SCINE) and a specific conda environment, which may limit portability but is clearly documented.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
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    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
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    "production agents without a repository review",
    "The SKILL.md excerpt is truncated, but the provided content is comprehensive and well-structured.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "The skill depends on external software (ORCA, SCINE) and a specific conda environment, which may limit portability but is clearly documented."
  ],
  "agent_contract": {
    "task_input": "Use chem-dft-orca-optimization in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 66/100 Manual review",
      "Audit: 73/100 Needs review",
      "Safety: 33/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "learningmatter-mit-chem-dft-orca-optimization (chem-dft-orca-optimization)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
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    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
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      "failed",
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      "blocked_by_risk",
      "setup_required"
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      "task": "Use chem-dft-orca-optimization in an agent workflow",
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      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/learningmatter-mit-chem-dft-orca-optimization",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-dft-orca-optimization",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-dft-orca-optimization/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-dft-orca-optimization&task=Use%20chem-dft-orca-optimization%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-dft-orca-optimization%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-dft-orca-optimization%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-dft-orca-optimization/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-dft-orca-optimization"
  }
}

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