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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.
Overview
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
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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-agentwithscine_utilities,scine_readuct, andaseinstalled - ORCA binary: The environment variable
ORCA_BINARY_PATHmust point to the ORCA executableexport 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
# 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 convergedfinal_energy_hartree,final_energy_eV: Final electronic energyfinal_max_force_eV_per_Ang,final_rms_force_eV_per_Ang: Residual force informationopt_type: Whether this was a minimization or TS search- If
--calculate_final_hessianwas used:hessian_eV_per_Ang2,hessian_wave_numbers_cm-1, andn_imaginary_modes - All input parameters for reproducibility
initial_structure.xyz: Copy of the input structureoptimized_structure.xyz: The optimized geometry
5. Interpreting Results
Minimization
- Check
converged: truein the results JSON. - Residual forces should be small (max force < 0.01 eV/A for typical convergence).
- If convergence fails, try increasing
--convergence_max_iterationsor 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_hessianto compute the Hessian directly after optimization. The output will includen_imaginary_modes— expect exactly 1 for a valid TS. - Alternatively, run a separate single-point Hessian with the singlepoint skill using
--compute_hessian.
- Recommended: Use
- 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_PATHmust be set and point to a working ORCA installation. - Environment: All commands require the
orca-agentconda 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
File metadata
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]
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---
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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- Source repository
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- Version
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- Last GitHub push
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"label": "No agent outcome data yet"
},
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},
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"agent-skill"
],
"known_risks": [
"The SKILL.md excerpt is truncated, but the provided content is comprehensive and well-structured.",
"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"
]
},
"agent_proven": {
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"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,
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"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
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},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
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"score": 73,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"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.",
"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"
]
},
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"label": "Blocked for auto-install",
"auto_install_policy": "block",
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"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": "2mo since push",
"risk": "Needs review"
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"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",
"The skill depends on external software (ORCA, SCINE) and a specific conda environment, which may limit portability but is clearly documented.",
"Quality score needs review"
],
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"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
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"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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"method": "POST",
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"skill_slug": "learningmatter-mit-chem-dft-orca-optimization",
"task": "Use chem-dft-orca-optimization in an agent workflow",
"agent": "codex",
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"task_success": true,
"output_quality": 4,
"error_type": null,
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"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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},
"endpoints": {
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"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"
}
}For the creator
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- learningmatter-mit
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