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Agent skill

Mopac

by lamm-mit in lamm-mit/scienceclaw

Semi-empirical quantum chemistry with MOPAC. An agent skill from lamm-mit/scienceclaw.

LGPL-3.0Auto-check passedResearch & Science

Install Mopac

skills CLI
$ npx skills add lamm-mit/scienceclaw --skill mopac -a claude-code

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

GitHub CLI
$ gh skill install lamm-mit/scienceclaw mopac --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/lamm-mit/scienceclaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/mopac .claude/skills/mopac && 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
mopac
GitHub stars
244
Token cost
~1.7k tokens
SKILL.md length
487 words
Files
5 (incl. scripts)
Skills in repo
85
Repo updated
First seen
Licence
LGPL-3.0

At a glance

Semi-empirical quantum chemistry with MOPAC. An agent skill from lamm-mit/scienceclaw.

  • Works in 5 steps: Geometry Optimization → Transition State Finding → Molecular Properties → …
  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Overview, Core Capabilities, MOPAC Methods and Use Cases, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Mopac is an agent skill from lamm-mit/scienceclaw. Semi-empirical quantum chemistry with MOPAC. Fast QM calculations for geometry optimization, properties, activation barriers, reaction pathways. Methods PM6, PM7, PM6-D3H4X for 1000x faster than DFT. For full DFT accuracy, use ase. For classical MD, use openmm.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts (for example `scripts/mopac_optimize.py` and `scripts/mopac_properties.py`).

It sits in Research & Science, covering Drug discovery and cheminformatics. The licence is LGPL-3.0.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/mopac”

Requirements

  • Python 3

Workflow steps

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

  1. Geometry Optimization
  2. Transition State Finding
  3. Molecular Properties
  4. Solvation Effects
  5. Vibrational Analysis

What it can do on your machine

Read from SKILL.md and the folder at commit ab9aba1. 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 4 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    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):

    • openmopac.net

    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

Mopac loads about 1.7k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 487 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

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 lamm-mit/scienceclaw at commit ab9aba1, republished under its LGPL-3.0 licence (© lamm-mit). 487 words, ~1,683 tokens.

Download SKILL.mdSave it as .claude/skills/mopac/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
mopac
description
Semi-empirical quantum chemistry with MOPAC. Fast QM calculations for geometry optimization, properties, activation barriers, reaction pathways. Methods PM6, PM7, PM6-D3H4X for 1000x faster than DFT. For full DFT accuracy, use ase. For classical MD, use openmm.
license
LGPL
metadata.skill-author
K-Dense Inc.
metadata.domain
computational-chemistry, quantum-chemistry

MOPAC Semi-Empirical Quantum Chemistry

Overview

MOPAC provides semi-empirical quantum chemistry calculations that are ~1000x faster than DFT while maintaining reasonable accuracy for many applications. This skill enables rapid computational investigation of reaction mechanisms, transition states, activation barriers, and molecular properties. MOPAC is ideal for high-throughput screening and rapid hypothesis testing in drug discovery and materials chemistry.

Core Capabilities

1. Geometry Optimization

Molecular Structures:

Optimize small molecules and drug-like compounds:

python
# PM6 method (balanced speed/accuracy)
# PM7 method (improved accuracy, slightly slower)
# PM6-D3H4X (includes dispersion corrections for D3 interactions)

mopac_optimized = optimize_structure(
    smiles="CCO",
    method="PM6",
    convergence="tight"
)

# Get optimized geometry and energy
energy = mopac_optimized.get_energy()
structure = mopac_optimized.get_structure()

Key Features:

  • Fast convergence (seconds to minutes)
  • Suitable for molecules up to ~100 atoms efficiently
  • Can handle aromatic, heterocyclic, organometallic systems
  • Includes hydrogen bonding, van der Waals interactions
2. Transition State Finding

Activation Barriers:

Locate and characterize transition states:

python
# Min-TS-Min pathway
# 1. Optimize reactant
# 2. Find transition state (TS keyword in MOPAC)
# 3. Optimize product

ts_structure = find_transition_state(
    reactant="reactant.smi",
    product="product.smi",
    method="PM6"
)

activation_barrier = ts_structure.get_energy() - reactant.get_energy()

Applications:

  • Drug metabolism prediction (phase I/II mechanisms)
  • Chemical reactivity assessment
  • Reaction selectivity prediction
  • Mechanistic hypothesis validation
3. Molecular Properties

Calculate from Quantum Wavefunction:

python
- Dipole moment
- Polarizability
- Electronegativity
- Electrostatic potential (ESP)
- Partial charges (Mulliken, Löwdin)
- Orbital energies (HOMO, LUMO, gap)
- Hardness, softness (chemical potential)
- Reactivity indices (Fukui functions)

For Drug Design:

  • Predict pKa (using COSMO solvation)
  • Estimate metabolic susceptibility
  • Assess chemical stability
  • Identify reactive hot spots
4. Solvation Effects

COSMO Implicit Solvent Model:

Calculate properties in aqueous/organic media:

python
# COSMO (Conductor-like Screening Model)
# Implicit solvent descriptions for:
# - Water
# - DMSO, DMF
# - Chloroform, dichloromethane
# - Alcohols

aqueous_energy = calculate_solvation(
    structure="molecule.xyz",
    solvent="water",
    method="PM6"
)

# pKa prediction from desolvation energy
5. Vibrational Analysis

Frequencies and Thermochemistry:

Compute IR-active vibrational modes:

python
- Vibrational frequencies
- Infrared intensities
- Raman scattering
- Zero-point energy (ZPE)
- Enthalpy corrections
- Entropy corrections
- Gibbs free energy at any temperature

MOPAC Methods

PM6 (Parametrized Model 6)
  • Accuracy: Good for organics, heteroatoms, hydrogen bonding
  • Speed: Very fast (~seconds)
  • Use: General purpose, drug discovery, screening
PM7
  • Accuracy: Improved over PM6 (metallorganic, transition metals)
  • Speed: Slightly slower than PM6 (~10-30 seconds)
  • Use: More accurate properties, metal-containing systems
PM6-D3H4X
  • Accuracy: Best for weak interactions (dispersion, H-bonding)
  • Speed: ~2x PM6
  • Use: Noncovalent interactions, supramolecular chemistry, protein-ligand docking

Use Cases

Drug Discovery:

  • ADMET property prediction
  • Metabolite structure elucidation
  • pKa/logP calculations
  • Off-target toxicity prediction (reactivity-based)

Reaction Mechanism:

  • Identify rate-limiting step
  • Predict regioselectivity
  • Understand stereoselectivity
  • Design synthetic routes

Materials Chemistry:

  • Conjugation in organic semiconductors
  • Band gap prediction for dyes
  • Hydrogen storage materials
  • Supramolecular assembly design

Chemical Stability:

  • Hydrolysis rates
  • Oxidative degradation pathways
  • Shelf-life prediction
  • Storage condition optimization
Show full SKILL.md (197 more words)Show less

Integration with Other Skills

Input:

  • SMILES from pubchem (convert to structures)
  • Protein residues from uniprot (study interaction mechanisms)
  • Known ligands from chembl (understand binding mechanisms)

Output:

  • Transition states for mechanistic understanding
  • Properties for ML model training
  • Reactivity predictions for chemical screening
  • Activation barriers for kinetic modeling

Accuracy vs. DFT

PropertyMOPAC (PM6)DFTErrorTime (MOPAC)Time (DFT)
Geometry0.02 Å0.01 ű0.01 Å5 sec5 min
Energy±1-2 eV±0.1 eV±1 eV5 sec5 min
Barriers±2-4 kcal±0.5 kcal±2 kcal30 sec2-4 hrs
pKa±0.5 units±0.3 units±0.520 sec30 min

When to use MOPAC:

  • ✅ Fast screening (drug discovery)
  • ✅ Reaction mechanism validation
  • ✅ Property prediction (pKa, dipole)
  • ✅ Large molecule ensembles

When to use DFT:

  • ✅ High accuracy needed
  • ✅ Weak interactions (dispersion)
  • ✅ Excited state chemistry
  • ✅ Periodic systems (crystals)

Limitations

  • Parameterized for specific atom types (main group + some transition metals)
  • Less accurate for charged species or transition metals
  • Cannot predict NMR shifts or electron density maps
  • No excited states (use DFT)
  • Parameterization based on training set (may have extrapolation errors)

Example Workflow

bash
# 1. Optimize structure
python mopac_optimize.py --smiles "CC(C)CC(N)C(=O)O" --method PM6

# 2. Calculate properties
python mopac_properties.py --structure optimized.xyz --include-frequencies

# 3. Find transition state
python mopac_transition_state.py --reactant reactant.xyz --product product.xyz

# 4. Predict pKa
python mopac_pka.py --structure molecule.xyz --solvent water

# 5. Analyze reactivity
python mopac_reactivity.py --structure molecule.xyz --method PM6-D3H4X

References

© lamm-mit, LGPL-3.0. 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 4 other files (scripts) in skills/mopac of lamm-mit/scienceclaw.

  • SKILL.md
  • scripts/__pycache__/mopac_optimize.cpython-313.pyc
  • scripts/__pycache__/mopac_properties.cpython-313.pyc
  • scripts/mopac_optimize.py
  • scripts/mopac_properties.py

Open the folder on GitHubat commit ab9aba1

Compare with similar skills

Mopac next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Mopac this skilllamm-mit/scienceclaw244—~1.7kAutomated safety check: PassLGPL-3.0
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DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Biomedical Analysis Dispatchxjtulyc/MedgeClaw6171 repos~2kAutomated safety check: PassNone
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Mopac

What does Mopac do?

Semi-empirical quantum chemistry with MOPAC. An agent skill from lamm-mit/scienceclaw. Mopac is an agent skill from lamm-mit/scienceclaw. Semi-empirical quantum chemistry with MOPAC.

When should I use Mopac?

Mopac fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Mopac in Claude Code?

Run `npx skills add lamm-mit/scienceclaw --skill mopac -a claude-code`. Or copy the skill folder (skills/mopac in lamm-mit/scienceclaw) into .claude/skills/mopac in your project. Claude Code loads it when a task matches its description.

How do I install Mopac in Codex?

Run `npx skills add lamm-mit/scienceclaw --skill mopac -a codex`. Or copy the skill folder (skills/mopac in lamm-mit/scienceclaw) into .agents/skills/mopac in your project. Codex loads it when a task matches its description.

Can I use Mopac 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 lamm-mit/scienceclaw --skill mopac -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mopac, .gemini/skills/mopac, .github/skills/mopac and .opencode/skills/mopac in your project.

What does Mopac need to run?

Going by SKILL.md and its folder, Mopac needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Mopac access the network?

SKILL.md names 1 domain. As links in the text: openmopac.net. This is read from the text; nothing was executed.

Is Mopac 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 Mopac use?

Mopac is published under the LGPL-3.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mopac use?

About 1.7k tokens (SKILL.md is roughly 6.7k 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 Mopac?

Skills that share tags, products or a category with Mopac: Molecode (AtomFlow-AI/MoleCode, 305 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mopac?

lamm-mit (a GitHub user) maintains it in lamm-mit/scienceclaw, which has 244 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 21, 2026.

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