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chem-dft-orca-advanced-calculation

Write and run custom ORCA input files for advanced electronic structure methods or settings not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic effects, advanced SCF, NMR/EPR, and more.

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

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

Advanced ORCA Calculation

Goal

Enable advanced ORCA quantum chemistry calculations by constructing a custom ORCA input file from scratch. This skill covers methods and features not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic effects, advanced SCF settings, NMR/EPR properties, and more.

[!IMPORTANT] For standard DFT single-point calculations (energy, gradients, Hessian), use the singlepoint skill instead. For geometry optimization, use the optimization skill. This skill is for cases where those wrappers do not expose the needed method or settings.

1. Prerequisites

Conda environment: orca-agent with ase installed (SCINE not required for this skill)
ORCA binary: The environment variable ORCA_BINARY_PATH must point to the ORCA executable
```
export ORCA_BINARY_PATH=/path/to/orca
```
ORCA documentation: Consult the ORCA 6.1 tutorials for method-specific input syntax, keyword blocks, and recommended settings

2. Workflow

Step 1: Understand the user's request

Identify the target method, property, and system. Determine which ORCA keywords and blocks are needed. If unsure, consult the tutorials linked above for the specific method.

Step 2: Write the ORCA input file

Create a .inp file following ORCA input syntax. Every input file should include:

Mandatory elements:

A keyword line starting with ! specifying the method, basis set, and job type
A *xyzfile entry referencesing an external .xyz file

Strongly recommended elements:

%pal nprocs N end: parallelization (always set this to avoid single-core runs)
%maxcore M: memory per core in MB (e.g. 4000 for 4 GB per core)

Example — TD-DFT excited states:

! B3LYP def2-TZVP TightSCF
%pal nprocs 4 end
%maxcore 4000

%tddft
  NRoots 10
  MaxDim 5
end

* xyzfile 0 1 molecule.xyz

Example: DLPNO-CCSD(T) single point:

! DLPNO-CCSD(T) def2-TZVPP def2-TZVPP/C TightSCF
%pal nprocs 8 end
%maxcore 4000

* xyzfile 0 1 molecule.xyz

Example: Geometry optimization with frequency calculation:

! B3LYP def2-TZVP D3BJ Opt Freq TightSCF
%pal nprocs 4 end
%maxcore 4000

* xyzfile 0 1 molecule.xyz

Example: CASSCF multi-reference:

! CASSCF def2-TZVP
%pal nprocs 4 end
%maxcore 8000

%casscf
  nel 6
  norb 6
  nroots 3
end

* xyzfile 0 1 molecule.xyz

[!TIP] When using an external .xyz file with * xyzfile charge mult filename.xyz, the .xyz file must be placed in the same directory where ORCA runs (the --output_dir).

Step 3: Run the calculation

# Env: orca-agent
python .agent/skills/chem-dft-orca-advanced-calculation/scripts/run_orca_input.py \
    --input_file calculation.inp \
    --output_dir research/my_project/advanced_calc

The script will:

Validate basic input structure and warn about missing %pal/%maxcore
Copy the input file to the output directory
Execute ORCA and capture all output
Parse the final electronic energy from the output
Save a calculation_results.json summary

Step 4: Parse results

For standard energies, the runner script already extracts the final energy. For other properties, use the dedicated parser:

# Env: orca-agent
python .agent/skills/chem-dft-orca-advanced-calculation/scripts/parse_orca_output.py \
    --output_file research/my_project/advanced_calc/calculation.out \
    --property energy orbitals

Available --property options in the parser:

energy: Final energy, nuclear repulsion, dispersion correction
orbitals: Orbital energies, HOMO/LUMO, gap
frequencies: Vibrational frequencies, imaginary modes, IR intensities
thermochemistry: ZPE, enthalpy, Gibbs energy, entropy
all: Parse everything available

Step 5: Manual output inspection

For properties not covered by the built-in parser (excited-state energies, NMR shifts, spin populations, natural orbitals, etc.), read the ORCA calculation.property.txt file directly.

3. Common Use Cases

Method	Key ORCA Keywords	Notes
Multi-step SCF convergence	`! GuessMode=CMatrix`	Some SCFs are difficult to converge, solving it multiple steps, converging first on a small basis set and loose criterion and launching it again with the desired parameters
TD-DFT excited states	`! B3LYP def2-TZVP`, `%tddft NRoots N end`	Use TDA for faster approximation
CASSCF/NEVPT2	`! CASSCF def2-TZVP`, `%casscf nel N norb M end`	Active space selection is critical
DFT + NMR	`! B3LYP def2-TZVP NMR`	Shielding tensors in output
DFT + EPR	`! B3LYP def2-TZVP EPR/ORP`	g-tensor and hyperfine couplings
Relativistic (ZORA)	`! B3LYP ZORA def2-TZVP SARC/J`	For heavy elements; use SARC basis sets
Scan/Relaxed scan	`! B3LYP def2-SVP Opt`, `%geom Scan ... end`	Potential energy surface scans

4. Output Files

calculation_results.json: Summary with energy, SCF convergence, return code, and any input warnings
<input_stem>.property.txt: Structured ORCA output file of all properties
<input_stem>.out: Full ORCA output file, only suitable for debugging errors
Various ORCA-generated files (.gbw, .densities, .engrad, etc.) in the output directory
parsed_results.json (if parser was run): Structured extraction of requested properties

5. Constraints

Input correctness: The agent is responsible for writing a valid ORCA input file. The runner performs basic validation but cannot catch all syntax errors, ORCA itself will report those in the output.
SCF convergence: Always check that the SCF converged. If it did not, try SlowConv, VerySlowConv, or adjust %scf MaxIter and damping settings.
Memory: ORCA can be memory-intensive for correlated methods. Set %maxcore appropriately (rule of thumb: total available RAM / nprocs, leaving some for the OS).
Disk: Post-HF methods (CCSD(T), CASSCF) can generate large temporary files. Ensure sufficient disk space.
ORCA binary: ORCA_BINARY_PATH must be set and point to a working ORCA installation.
Environment: All commands require the orca-agent conda environment.
Parallelization: ORCA uses OpenMPI internally. Do not run multiple ORCA instances on overlapping core sets.
Output parsing: The built-in parser covers common output patterns. For uncommon methods or output formats, the raw .out file must be inspected directly.

References

Neese, F., "Software update: The ORCA program system—Version 5.0", WIREs Comput. Mol. Sci., 2022. DOI
ORCA 6.1 Tutorials: https://www.faccts.de/docs/orca/6.1/tutorials/

Author: Miguel Steiner Contact: GitHub @steinmig

Ver fuente ↗

Metadatos del archivo

name: chem-dft-orca-advanced-calculation
description: Write and run custom ORCA input files for advanced electronic structure methods or settings not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic effects, advanced SCF, NMR/EPR, and more.
category: [chemistry]

Ver texto original

---
name: chem-dft-orca-advanced-calculation
description: Write and run custom ORCA input files for advanced electronic structure methods or settings not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic effects, advanced SCF, NMR/EPR, and more.
category: [chemistry]
---

# Advanced ORCA Calculation

## Goal

Enable advanced ORCA quantum chemistry calculations by constructing a custom ORCA input file from scratch. This skill covers methods and features not available through the SCINE wrapper, including multi-reference methods, excited states, relativistic effects, advanced SCF settings, NMR/EPR properties, and more.

> [!IMPORTANT]
> For **standard DFT single-point** calculations (energy, gradients, Hessian), use the [singlepoint skill](../chem-dft-orca-singlepoint/SKILL.md) instead. For **geometry optimization**, use the [optimization skill](../chem-dft-orca-optimization/SKILL.md). This skill is for cases where those wrappers do not expose the needed method or settings.

## 1. Prerequisites

- **Conda environment:** `orca-agent` with `ase` installed (SCINE not required for this skill)
- **ORCA binary:** The environment variable `ORCA_BINARY_PATH` must point to the ORCA executable
  ```bash
  export ORCA_BINARY_PATH=/path/to/orca
  ```
- **ORCA documentation:** Consult the [ORCA 6.1 tutorials](https://www.faccts.de/docs/orca/6.1/tutorials/index.html) for method-specific input syntax, keyword blocks, and recommended settings

## 2. Workflow

### Step 1: Understand the user's request

Identify the target method, property, and system. Determine which ORCA keywords and blocks are needed. If unsure, consult the tutorials linked above for the specific method.

### Step 2: Write the ORCA input file

Create a `.inp` file following ORCA input syntax. Every input file should include:

**Mandatory elements:**
- A keyword line starting with `!` specifying the method, basis set, and job type
- A `*xyzfile` entry referencesing an external .xyz file

**Strongly recommended elements:**
- `%pal nprocs N end`: parallelization (always set this to avoid single-core runs)
- `%maxcore M`: memory per core in MB (e.g. 4000 for 4 GB per core)

**Example — TD-DFT excited states:**
```
! B3LYP def2-TZVP TightSCF
%pal nprocs 4 end
%maxcore 4000

%tddft
  NRoots 10
  MaxDim 5
end

* xyzfile 0 1 molecule.xyz
```

**Example: DLPNO-CCSD(T) single point:**
```
! DLPNO-CCSD(T) def2-TZVPP def2-TZVPP/C TightSCF
%pal nprocs 8 end
%maxcore 4000

* xyzfile 0 1 molecule.xyz
```

**Example: Geometry optimization with frequency calculation:**
```
! B3LYP def2-TZVP D3BJ Opt Freq TightSCF
%pal nprocs 4 end
%maxcore 4000

* xyzfile 0 1 molecule.xyz
```

**Example: CASSCF multi-reference:**
```
! CASSCF def2-TZVP
%pal nprocs 4 end
%maxcore 8000

%casscf
  nel 6
  norb 6
  nroots 3
end

* xyzfile 0 1 molecule.xyz
```

> [!TIP]
> When using an external `.xyz` file with `* xyzfile charge mult filename.xyz`, the `.xyz` file must be placed in the same directory where ORCA runs (the `--output_dir`).

### Step 3: Run the calculation

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-advanced-calculation/scripts/run_orca_input.py \
    --input_file calculation.inp \
    --output_dir research/my_project/advanced_calc
```

The script will:
1. Validate basic input structure and warn about missing `%pal`/`%maxcore`
2. Copy the input file to the output directory
3. Execute ORCA and capture all output
4. Parse the final electronic energy from the output
5. Save a `calculation_results.json` summary

### Step 4: Parse results

For standard energies, the runner script already extracts the final energy. For other properties, use the dedicated parser:

```bash
# Env: orca-agent
python .agent/skills/chem-dft-orca-advanced-calculation/scripts/parse_orca_output.py \
    --output_file research/my_project/advanced_calc/calculation.out \
    --property energy orbitals
```

Available `--property` options in the parser:
- `energy`: Final energy, nuclear repulsion, dispersion correction
- `orbitals`: Orbital energies, HOMO/LUMO, gap
- `frequencies`: Vibrational frequencies, imaginary modes, IR intensities
- `thermochemistry`: ZPE, enthalpy, Gibbs energy, entropy
- `all`: Parse everything available

### Step 5: Manual output inspection

For properties not covered by the built-in parser (excited-state energies, NMR shifts, spin populations, natural orbitals, etc.), read the ORCA `calculation.property.txt` file directly.

## 3. Common Use Cases

| Method                     | Key ORCA Keywords                           | Notes                                                                                                                                                                                                        |
|----------------------------|---------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Multi-step SCF convergence | `! GuessMode=CMatrix` | Some SCFs are difficult to converge, solving it multiple steps, converging first on a small basis set and loose criterion and launching it again with the desired parameters |
| TD-DFT excited states      | `! B3LYP def2-TZVP`, `%tddft NRoots N end`  | Use TDA for faster approximation                                                                                                                                                                             |
| CASSCF/NEVPT2              | `! CASSCF def2-TZVP`, `%casscf nel N norb M end` | Active space selection is critical                                                                                                                                                                           |
| DFT + NMR                  | `! B3LYP def2-TZVP NMR`                     | Shielding tensors in output                                                                                                                                                                                  |
| DFT + EPR                  | `! B3LYP def2-TZVP EPR/ORP`                 | g-tensor and hyperfine couplings                                                                                                                                                                             |
| Relativistic (ZORA)        | `! B3LYP ZORA def2-TZVP SARC/J`             | For heavy elements; use SARC basis sets                                                                                                                                                                      |
| Scan/Relaxed scan          | `! B3LYP def2-SVP Opt`, `%geom Scan ... end` | Potential energy surface scans                                                                                                                                                                               |

## 4. Output Files

- `calculation_results.json`: Summary with energy, SCF convergence, return code, and any input warnings
- `<input_stem>.property.txt`: Structured ORCA output file of all properties
- `<input_stem>.out`: Full ORCA output file, only suitable for debugging errors
- Various ORCA-generated files (`.gbw`, `.densities`, `.engrad`, etc.) in the output directory
- `parsed_results.json` (if parser was run): Structured extraction of requested properties

## 5. Constraints

- **Input correctness:** The agent is responsible for writing a valid ORCA input file. The runner performs basic validation but cannot catch all syntax errors, ORCA itself will report those in the output.
- **SCF convergence:** Always check that the SCF converged. If it did not, try `SlowConv`, `VerySlowConv`, or adjust `%scf MaxIter` and damping settings.
- **Memory:** ORCA can be memory-intensive for correlated methods. Set `%maxcore` appropriately (rule of thumb: total available RAM / nprocs, leaving some for the OS).
- **Disk:** Post-HF methods (CCSD(T), CASSCF) can generate large temporary files. Ensure sufficient disk space.
- **ORCA binary:** `ORCA_BINARY_PATH` must be set and point to a working ORCA installation.
- **Environment:** All commands require the `orca-agent` conda environment.
- **Parallelization:** ORCA uses OpenMPI internally. Do not run multiple ORCA instances on overlapping core sets.
- **Output parsing:** The built-in parser covers common output patterns. For uncommon methods or output formats, the raw `.out` file must be inspected directly.

## 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)
- ORCA 6.1 Tutorials: [https://www.faccts.de/docs/orca/6.1/tutorials/](https://www.faccts.de/docs/orca/6.1/tutorials/index.html)

---

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

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Licencia: MIT
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Licencia: MIT

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
No critical security issues found. The skill runs ORCA, a legitimate scientific software, and does not execute arbitrary shell commands or access sensitive data.
The skill relies on an external ORCA binary and environment variable, which 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
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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Repositorio fuente: learningmatter-mit/AtomisticSkills
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 3 sept 2026
Registro actualizado: 6 sept 2026
Ruta de instrucciones: .agents/skills/chem-dft-orca-advanced-calculation/SKILL.md @ d1f7ecfda2cd

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

66/100

Prometedor

Confianza

58/100

Do not auto-install

Auditoría

73/100

Requiere revisión

Abrir auditoría completa Ver informe de evaluación

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
No critical security issues found. The skill runs ORCA, a legitimate scientific software, and does not execute arbitrary shell commands or access sensitive data.
The skill relies on an external ORCA binary and environment variable, which 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
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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Resultados: —

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      "license": "MIT",
      "repository": "https://github.com/learningmatter-mit/AtomisticSkills/tree/main/.agents/skills/chem-dft-orca-advanced-calculation",
      "install": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-advanced-calculation",
      "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": [
      "[chemistry]",
      "agent-skill"
    ],
    "known_risks": [
      "No critical security issues found. The skill runs ORCA, a legitimate scientific software, and does not execute arbitrary shell commands or access sensitive data.",
      "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"
    ]
  },
  "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",
      "No critical security issues found. The skill runs ORCA, a legitimate scientific software, and does not execute arbitrary shell commands or access sensitive data.",
      "The skill relies on an external ORCA binary and environment variable, which 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"
    ]
  },
  "safety_gate": {
    "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": "Coding and developer agents",
    "scenario": "Coding",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "No critical security issues found. The skill runs ORCA, a legitimate scientific software, and does not execute arbitrary shell commands or access sensitive data.",
    "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 relies on an external ORCA binary and environment variable, which is clearly documented."
  ],
  "agent_contract": {
    "task_input": "Use chem-dft-orca-advanced-calculation 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-advanced-calculation (chem-dft-orca-advanced-calculation)",
      "install_command": "npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-advanced-calculation",
      "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."
    }
  },
  "outcome_feedback": {
    "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",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
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      "skill_slug": "learningmatter-mit-chem-dft-orca-advanced-calculation",
      "task": "Use chem-dft-orca-advanced-calculation in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "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-advanced-calculation",
    "api": "https://www.openagentskill.com/api/agent/skills/learningmatter-mit-chem-dft-orca-advanced-calculation",
    "audit": "https://www.openagentskill.com/skills/learningmatter-mit-chem-dft-orca-advanced-calculation/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=learningmatter-mit-chem-dft-orca-advanced-calculation&task=Use%20chem-dft-orca-advanced-calculation%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20chem-dft-orca-advanced-calculation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20chem-dft-orca-advanced-calculation%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/learningmatter-mit-chem-dft-orca-advanced-calculation/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/learningmatter-mit-chem-dft-orca-advanced-calculation"
  }
}

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Creador: learningmatter-mit
Fuente: learningmatter-mit/AtomisticSkills
Indexado por: Índice comunitario de OpenAgentSkill

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