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DFT, molecular simulation, and reaction prediction tools for chemists
Passed 17 of 22 test cases with the skill, and 15 of 22 without it.
and 2 more in the full report
3 fixed14 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to set up a DFT quantum chemistry calculation on a small molecule using ORCA. Could you provide a formatted input file template for geometry optimization and vibrational frequencies, along with a Python script that parses the output text file to extract both the final single-point energy and the Gibbs free energy?
| Checked | Without skill | With skill |
|---|---|---|
| The ORCA input template specifies the route line ! B3LYP def2-TZVP D3BJ OPT FREQ. | Missed | Passed |
| The Python script uses a regular expression searching for 'FINAL SINGLE POINT ENERGY' to extract total energy. | Passed | Passed |
| The Python script converts Hartree to kcal/mol using the multiplier 627.509. | Missed | Passed |
The instructions your model reads when the skill loads.
Computational chemistry bridges quantum mechanics and practical chemistry, enabling researchers to predict molecular properties, reaction mechanisms, and material behaviors without stepping into a wet lab. From drug design to catalyst optimization, computational methods accelerate discovery by screening thousands of candidates before committing to synthesis.
This guide covers the major computational chemistry paradigms: Density Functional Theory (DFT) for electronic structure calculations, molecular dynamics (MD) for simulating atomic motion, machine learning potentials for scaling up simulations, and reaction prediction tools for retrosynthesis and mechanism elucidation. Each section includes tool recommendations, typical workflows, and code examples.
Whether you are a chemistry PhD student running your first Gaussian calculations, a materials scientist exploring new alloys with VASP, or a medicinal chemist using ML-based property prediction, this skill provides the conceptual framework and practical recipes to get productive quickly.
DFT is the workhorse of quantum chemistry. It provides a good balance of accuracy and computational cost for systems of up to a few hundred atoms.
| Property | DFT Suitability | Typical Error |
|---|---|---|
| Molecular geometry | Excellent | < 0.02 Angstrom |
| Vibrational frequencies | Good | 3-5% |
| Reaction barriers | Good with correction | 2-5 kcal/mol |
| Band gaps | Fair (tends to underestimate) | 0.5-1.0 eV |
| Van der Waals interactions | Requires dispersion correction | Varies |
| Excited states | Fair (TD-DFT) | 0.2-0.5 eV |
| Software | License | Strengths | Basis Sets |
|---|---|---|---|
| Gaussian | Commercial | Broad functionality, well-documented | Gaussian-type |
| ORCA | Free (academic) | DFT + wavefunction methods, excellent support | Gaussian-type |
| VASP | Commercial | Periodic systems, materials science | Plane-wave |
| Quantum ESPRESSO | Open source | Periodic DFT, phonons | Plane-wave |
| Psi4 | Open source | Reference implementations, Python API | Gaussian-type |
| CP2K | Open source | Mixed Gaussian/plane-wave, large systems | Mixed |
# geometry_optimization.inp
test cases passed, vs 15 / 22 without
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