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Agent skill

Chem Dft Orca Optimization

by learningmatter-mit in learningmatter-mit/AtomisticSkills

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

MITAuto-check passed

Install Chem Dft Orca Optimization

skills CLI
$ npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install learningmatter-mit/AtomisticSkills chem-dft-orca-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/chem-dft-orca-optimization .claude/skills/chem-dft-orca-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
chem-dft-orca-optimization
GitHub stars
175
Token cost
~2k tokens
SKILL.md length
714 words
Files
6 (incl. scripts)
Skills in repo
129
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 6 steps: Prerequisites → Parameters → Running an Optimization → …
  • SKILL.md covers Goal, Background, 1. Prerequisites and 2. Parameters, plus 5 more sections
  • Runs Python scripts from its folder

What it does

Chem Dft Orca Optimization is an agent skill from learningmatter-mit/AtomisticSkills. Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts (for example `example/README.md` and `scripts/run_optimization.py`).

The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.

Example prompts

  • “/chem-dft-orca-optimization”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Prerequisites
  2. Parameters
  3. Running an Optimization
  4. Output Files
  5. Interpreting Results
  6. Constraints

What it can do on your machine

Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • doi.org
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Chem Dft Orca Optimization loads about 2k tokens when it runs. Until then it costs about 37 tokens; SKILL.md has 714 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~37
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 714 words, ~1,952 tokens.

Download SKILL.mdSave it as .claude/skills/chem-dft-orca-optimization/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
chem-dft-orca-optimization
description
Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.
metadata.category
chemistry
metadata.venv
cpu

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

  • Environment: cpu (commands run through venv/run cpu ...), which includes scine_utilities, scine_readuct (x86_64 only), and ase
  • 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

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
bash
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/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
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/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
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/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
${CLAUDE_SKILL_DIR}/../../venv/run cpu python ${CLAUDE_SKILL_DIR}/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
Show full SKILL.md (294 more words)Show less

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 cpu 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

© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts) in skills/chem-dft-orca-optimization of learningmatter-mit/AtomisticSkills.

  • SKILL.md
  • example/README.md
  • example/h2o.xyz
  • example/initial_structure.xyz
  • example/ts_guess.xyz
  • scripts/run_optimization.py

Open the folder on GitHubat commit 7f2d86d

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Questions about Chem Dft Orca Optimization

What does Chem Dft Orca Optimization do?

Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper. Chem Dft Orca Optimization is an agent skill from learningmatter-mit/AtomisticSkills. Run DFT geometry optimization (minimization or TS search) on a molecular structure using ORCA via SCINE/ReaDuct wrapper.

How do I install Chem Dft Orca Optimization in Claude Code?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization -a claude-code`. Or copy the skill folder (skills/chem-dft-orca-optimization in learningmatter-mit/AtomisticSkills) into .claude/skills/chem-dft-orca-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Chem Dft Orca Optimization in Codex?

Run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization -a codex`. Or copy the skill folder (skills/chem-dft-orca-optimization in learningmatter-mit/AtomisticSkills) into .agents/skills/chem-dft-orca-optimization in your project. Codex loads it when a task matches its description.

Can I use Chem Dft Orca Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill chem-dft-orca-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chem-dft-orca-optimization, .gemini/skills/chem-dft-orca-optimization, .github/skills/chem-dft-orca-optimization and .opencode/skills/chem-dft-orca-optimization in your project.

What does Chem Dft Orca Optimization need to run?

Going by SKILL.md and its folder, Chem Dft Orca Optimization needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Chem Dft Orca Optimization access the network?

SKILL.md names 2 domains. As links in the text: doi.org and github.com. This is read from the text; nothing was executed.

Is Chem Dft Orca Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Chem Dft Orca Optimization use?

Chem Dft Orca Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Chem Dft Orca Optimization use?

About 2k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Chem Dft Orca Optimization?

Skills that share tags, products or a category with Chem Dft Orca Optimization: Minimalism (sickn33/agentic-awesome-skills, 47k stars), Orca (alsk1992/CloddsBot, 2.9k stars), Orca CLI (stablyai/orca, 87k stars) and Video Template Frame Build Minimal (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Chem Dft Orca Optimization?

learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.

Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.