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MOPAC is an open-source Fortran program for semiempirical quantum-chemistry calculations on molecules, crystals, and nanostructures. It can estimate properties such as heat of formation and optimize atomic coordinates at much lower computational cost than many higher-level approaches—but its simplified models are generally less accurate and less predictive. That makes MOPAC useful for exploration, screening, and some larger-system workflows, provided the method is validated for the property and system you care about.
What is MOPAC?
MOPAC stands for Molecular Orbital PACkage. It is a command-line program that uses semiempirical quantum methods to calculate chemical and physical properties. A typical run takes an input file describing a system with approximate atomic coordinates and returns an output file with results such as heat of formation and optimized coordinates. Keywords in the input file select calculations and control program behavior. The official project describes MOPAC as actively maintained and curated by the Molecular Sciences Software Institute (MolSSI). MOPAC project repository
The package is primarily used through input and output files, although the repository also documents an API for a subset of its functionality and provides examples. MOPAC grew from work on MNDO-family methods and is no longer limited to its early emphasis on organic-molecule thermochemistry in a vacuum.
What does semiempirical quantum chemistry mean in practice?
Semiempirical methods retain a quantum-mechanical description of electrons but simplify parts of the calculation and use parameters fitted to experimental data. These approximations reduce computational cost, making calculations feasible more quickly or for larger systems than many ab initio approaches. The corresponding tradeoff is that predictions depend on how well the model represents the target system and property; speed alone does not establish accuracy.
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A 2026 paper in the Journal of Open Source Software characterizes MOPAC semiempirical calculations as roughly 1,000 times faster but half as accurate as routine density functional theory (DFT) calculations. That is a broad contextual comparison from the paper, not a universal benchmark: actual performance and accuracy vary by method, system, and observable. 2026 JOSS paper on MOPAC
What is MOPAC used for?
The project’s scope has expanded to include solids, molecules in solution, most elements of the periodic table, and properties such as electronic spectroscopy. The 2026 JOSS paper also discusses biomolecular modeling, including the MOZYME localized molecular orbital solver and a model optimized for biomolecular applications. These capabilities make MOPAC relevant to more than small-molecule thermochemistry, but they do not guarantee suitability for every material, protein, or property.
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Commonly described use cases include interactive chemical exploration and teaching, high-throughput virtual screening, obtaining an initial estimate or checking a setup before a more expensive ab initio calculation, and some cost-sensitive protein modeling. Treat these as workflow examples, not claims that an unvalidated result is adequate for a particular scientific decision.
How should you choose between MOPAC and DFT?
Choose a method based on the calculation’s purpose, not just the system size or the speed headline. A fast approximate result can be valuable during exploration, while a final prediction may require a more accurate method—or direct validation against suitable data. The available broad comparison does not support a package-by-package benchmark or a guarantee for a particular MOPAC model.
- Computational cost: MOPAC’s central advantage is lower cost, which can help when screening many candidates or exploring larger systems.
- Target property and accuracy: Check whether the method has been validated for the property and chemical environment you need. Accuracy for one observable should not be assumed for another.
- System and workflow: Consider whether you are exploring, screening, modeling a biomolecule or material, or producing a final high-confidence prediction.
- Validation: Compare against relevant experimental data or appropriate higher-level calculations when the result will support an important conclusion.
How do you install MOPAC?
The official repository lists prebuilt releases for Linux, macOS, and Windows, a conda-forge package, and instructions for building from source. The repository release page showed MOPAC 23.2.5 as its latest standalone release when checked; consult it for current availability rather than inferring a newer standalone version from other product documentation. MOPAC releases
Install with conda-forge
- Open a terminal with conda installed and run
conda install -c conda-forge mopac. - Use the installed executable with a MOPAC input file; consult the repository’s documentation and examples for input syntax and supported keywords.
Use a prebuilt release
Choose the release asset for your operating system from the official releases page. Follow the installation notes provided with that release; available packaging and steps can differ by platform.
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Build from source
The project uses CMake. Its documented prerequisites are a Fortran compiler, BLAS/LAPACK, Python 3, and NumPy. MolSSI Driver Interface (MDI) engine support is optional and can be enabled with -DMDI=ON during configuration. Follow the repository’s current build instructions for the exact CMake workflow and platform-specific requirements. Build instructions in the MOPAC repository
Is MOPAC free and open source?
MOPAC is described by its official repository as an open-source program, and the project distributes software through its repository and release channels. Check the repository’s license and current distribution terms for the conditions that apply to your use. Official MOPAC repository
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The repository’s release page displayed version 23.2.5 as the latest standalone MOPAC release when checked. A separate Amsterdam Modeling Suite manual is labeled 2026.1 and describes an MOPAC engine sharing core routines with standalone MOPAC; 2026.1 is the suite manual’s version, not a standalone MOPAC release number. Standalone MOPAC releases · Amsterdam Modeling Suite MOPAC manual
How do you cite MOPAC?
For publications using the open-source program, the project requests citation of its 2026 JOSS paper:
J. E. Moussa and J. J. P. Stewart, “MOPAC: An open-source semiempirical molecular orbital program,” Journal of Open Source Software 11(119), 8025 (2026), DOI: 10.21105/joss.08025.
The project also permits citation of its Zenodo software archive, DOI: 10.5281/zenodo.6511958. Project citation instructions · MOPAC Zenodo archive
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