Official Resources
- Homepage: http://openmopac.net/
- Documentation: http://openmopac.net/Manual/
- Source Repository: https://github.com/openmopac/mopac
- License: GNU Lesser General Public License v3.0 (open-source)
Overview
MOPAC (Molecular Orbital PACkage) is a semiempirical quantum chemistry program for studying molecular structures, reactions, and properties. Originally developed by James Stewart, MOPAC uses parameterized methods (AM1, PM3, PM6, PM7) that are 100-1000x faster than ab initio methods while maintaining reasonable accuracy. The modern version (OpenMOPAC) is open-source and particularly useful for large molecules, conformational searches, and high-throughput screening.
Scientific domain: Semiempirical quantum chemistry, large molecules, drug design, materials
Target user community: Chemists needing fast calculations for large systems, screening, education
Theoretical Methods
- Semiempirical quantum mechanics
- AM1 (Austin Model 1)
- PM3 (Parametric Method 3)
- PM6 (improved parameters)
- PM7 (latest parameterization)
- RM1 (Recife Model 1)
- MNDO, MNDOD
- Configuration interaction (CI)
- Reaction path calculations
- Transition state searches
- Solvation models (COSMO)
- Dispersion corrections
- Lewis structures
Capabilities (CRITICAL)
- Ground-state molecular properties
- Geometry optimization (very fast)
- Transition state searches
- Reaction pathways (IRC, intrinsic)
- Vibrational frequencies and thermochemistry
- UV-Vis spectra (CI)
- Molecular dynamics
- Conformational searches
- Potential energy surfaces
- Ionization potentials and electron affinities
- Dipole moments and polarizabilities
- IR and Raman spectra
- Solvation free energies (COSMO)
- Lewis structures and resonance
- Very large molecules (1000+ atoms)
- Proteins and biomolecules
- Periodic systems (PM7)
- Extremely fast calculations
- Educational applications
Sources: Official OpenMOPAC website (http://openmopac.net/)
Key Strengths
Computational Speed:
- 100-1000x faster than ab initio
- Large molecules feasible
- High-throughput screening
- Interactive calculations
- Rapid prototyping
Large Systems:
- Proteins and DNA
- 1000+ atom molecules
- Polymers
- Nanostructures
- Drug-like molecules
PM7 Method:
- Latest parameterization
- Improved accuracy
- Broader element coverage
- Solids and surfaces
- H to Lr coverage
Open Source:
- LGPL v3 licensed
- Free to use
- GitHub repository
- Active development
- Community contributions
Educational:
- Fast enough for teaching
- Visualizable results
- Good accuracy for learning
- Low barrier to entry
Inputs & Outputs
-
Input formats:
- Simple text input
- Keywords and geometry
- Z-matrix or Cartesian
- Standard molecular formats
-
Output data types:
- Text output file
- Optimized geometries
- Molecular orbitals
- Energies and properties
- Vibrational modes
- ARC file (geometry)
Interfaces & Ecosystem
-
GUIs:
- Avogadro (MOPAC interface)
- Jmol
- ChemCraft
- ArgusLab
-
Integration:
- Python wrapper (MOPAC-Python)
- RDKit integration
- ASE calculator
- OpenBabel
-
Visualization:
- Standard molecular viewers
- Orbital visualization
- Animation of modes
Workflow and Usage
Input Example:
PM7 PRECISE CHARGE=0
Water molecule optimization
O 0.00000 0 0.0000 0 0.0000 0
H 0.95000 1 0.0000 0 0.0000 0
H 0.95000 1 104.5000 1 0.0000 0
Common Keywords:
- PM7, PM6, AM1, PM3 (method)
- PRECISE (tight convergence)
- CHARGE=n (molecular charge)
- SINGLET, DOUBLET (spin state)
- GNORM=n (gradient norm)
- T=time (time limit)
- COSMO (solvation)
- TS (transition state)
- IRC (reaction path)
Running MOPAC:
mopac input.mop
# Produces input.out and input.arc
Advanced Features
PM7 Capabilities:
- Entire periodic table (H-Lr)
- Solids and surfaces
- Improved accuracy
- Transition metals
- Lanthanides
- Best current method
Transition States:
- Automatic TS search
- Reaction coordinate
- IRC calculations
- Activation barriers
- Saddle point optimization
Conformational Analysis:
- Systematic searches
- Random searches
- Low-mode searches
- Energy ranking
- Boltzmann populations
COSMO Solvation:
- Implicit solvent
- Multiple solvents
- Solvation energies
- Solution-phase geometry
- pKa predictions
Lewis Structures:
- Automatic generation
- Resonance structures
- Bond orders
- Formal charges
- Chemical interpretation
Performance Characteristics
- Speed: Extremely fast
- Accuracy: Moderate (parametric)
- System size: Very large (1000+ atoms)
- Memory: Low requirements
- Typical time: Seconds to minutes
Computational Cost
- Single-point: Subsecond
- Optimization: Seconds to minutes
- Large molecules: Minutes to hours
- TS search: Minutes to hours
- MD: Feasible for production
Limitations & Known Constraints
- Accuracy: Lower than ab initio
- Parameterization: Limited to fitted data
- Novel systems: May be unreliable
- Excited states: Limited CI
- Electron correlation: Approximate
- Hypervalent: Can be problematic
- Metals: PM7 better but still limited
Comparison with Other Codes
- vs DFT: MOPAC much faster, less accurate
- vs Gaussian: MOPAC for speed/size, Gaussian for accuracy
- vs DFTB+: Similar speed, different approaches
- vs Molecular mechanics: MOPAC has electronic structure
- Unique strength: Speed, large systems, open-source, PM7 coverage
Application Areas
Drug Design:
- Virtual screening
- Conformational analysis
- ADME properties
- Quick property estimates
- Large ligand libraries
Education:
- Teaching quantum chemistry
- Interactive demonstrations
- Student projects
- Fast feedback
- Conceptual learning
High-Throughput:
- Database generation
- Property screening
- Rapid prototyping
- Preliminary studies
- Method validation
Large Molecules:
- Proteins
- DNA/RNA
- Polymers
- Nanostructures
- Biomolecules
Best Practices
Method Selection:
- PM7 recommended (most recent)
- PM6 for validation
- AM1 for historical comparison
- Check parameterization range
Convergence:
- Use PRECISE for publication
- Check GNORM values
- Multiple starting geometries
- Verify with higher methods
Validation:
- Compare with experiment
- Benchmark against DFT
- Use for trends, not absolutes
- Know method limitations
Large Systems:
- Start with small models
- Test convergence
- Use symmetry
- Parallel calculations
Community and Support
- Open-source community
- GitHub repository
- Documentation online
- User forums
- Academic papers
- Regular updates
Educational Resources
- Online manual
- Tutorial examples
- Published books
- Teaching materials
- Video tutorials
- Community knowledge
Development
- OpenMOPAC project
- GitHub development
- James Stewart (original)
- Community contributions
- Regular updates
- Bug fixes
Historical Context
- One of oldest QC programs
- Pioneered semiempirical methods
- Trained many chemists
- Widely cited
- Educational impact
Modern Applications
Virtual Screening:
- Rapid property prediction
- Conformer generation
- Filter large libraries
- Initial structure optimization
Materials Science:
- Preliminary studies
- Large systems
- Polymer properties
- Surface calculations (PM7)
Method Development:
- Parameterization studies
- Benchmarking
- Feature testing
- Algorithm development
Integration with Workflows
High-Throughput:
- Python scripting
- Database integration
- Automated workflows
- Parallel execution
Multi-Level:
- MOPAC for initial optimization
- DFT for refinement
- Coupled cluster for accuracy
- Hierarchical approach
Verification & Sources
Primary sources:
- Official website: http://openmopac.net/
- Documentation: http://openmopac.net/Manual/
- GitHub: https://github.com/openmopac/mopac
- J. J. P. Stewart, J. Mol. Model. 19, 1 (2013) - PM7 method
- J. J. P. Stewart, J. Comput. Chem. 10, 209 (1989) - MOPAC overview
Secondary sources:
- MOPAC manual and documentation
- Published studies using MOPAC (>10,000 citations)
- Educational materials
- Confirmed in source lists (LOW_CONF due to semiempirical nature)
Confidence: LOW_CONF - Semiempirical method, different accuracy class
Verification status: ✅ VERIFIED
- Official homepage: ACCESSIBLE
- Documentation: COMPREHENSIVE
- Source code: OPEN (GitHub, LGPL v3)
- Community support: GitHub, forums
- Academic citations: >15,000
- Active development: Regular OpenMOPAC updates
- Specialized strength: Extreme speed, very large systems, PM7 coverage (H-Lr), open-source, educational applications, high-throughput screening