3D Printable Molecular Models

Methods

Molecular geometries were obtained through a two-stage optimization pipeline implemented in a custom Python script. Starting from SMILES notation, the script generates ~2,000 conformers using the Merck Molecular Force Field (MMFF94) [1] to broadly sample the conformational space. The top 10 lowest-energy conformers from this classical screening are then refined using density functional theory (DFT) at the ωB97X-D/def2-SVP level [2]. The lowest-energy DFT-optimized structure is selected as the final geometry. Atoms are rendered as spheres scaled to van der Waals radii [3], and bond orders correspond to the dominant resonance contributor as encoded in the input SMILES. Each model is visually verified against PubChem [4], exported as STL, and manually colored with the CPK convention in a slicer. Running on an Intel Core i7-14700T using all 20 cores, the pipeline averaged approximately 10 minutes per molecule.

Force Fields

The initial conformational search relies on MMFF94, a well-established classical force field parameterized against high-level ab initio data for a broad range of organic and drug-like molecules [1]. MMFF94 describes the potential energy surface through analytical terms for bond stretching, angle bending, torsional rotation, van der Waals interactions, and electrostatics. Its parameters were derived by fitting to HF/6-31G* geometries and MP2-level energetics, which gives it reliable accuracy for conformer ranking at a fraction of the cost of quantum mechanical methods [5]. This makes it well suited for rapidly screening thousands of candidate geometries before passing a small subset to DFT refinement.

Density Functional Theory

Final geometry optimizations are performed with the ωB97X-D functional [6] paired with the def2-SVP basis set [7], executed through the Psi4 electronic structure package [2]. ωB97X-D is a range-separated hybrid functional that includes an empirical dispersion correction, making it particularly effective for capturing both covalent bonding and non-covalent intramolecular interactions that influence molecular conformation. The def2-SVP basis set provides a balanced trade-off between computational cost and accuracy for geometry optimizations of this kind. This level of theory has been widely benchmarked and shown to produce reliable equilibrium geometries for organic molecules [8].

Printing Notes

• Material: Optimized/tested for print with Bambu PLA and Elegoo PLA+.
• Style: Ball-and-stick type (easiest to color, rigid, aesthetic).
• Orientation: Oriented for optimal strength/waste/time compromise.
• Supports: Removable with care. Suggest using PETG as a support interface.

References

1. Halgren, T. A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem. 1996, 17, 490–519.
2. Smith, D. G. A.; Burns, L. A.; Simmonett, A. C.; Parrish, R. M.; Schieber, M. C.; et al. Psi4 1.4: Open-Source Software for High-Throughput Quantum Chemistry. J. Chem. Phys. 2020, 152, 184108.
3. Bondi, A. Van der Waals Volumes and Radii. J. Phys. Chem. 1964, 68, 441–451.
4. PubChem, National Library of Medicine, National Center for Biotechnology Information. https://pubchem.ncbi.nlm.nih.gov/
5. Halgren, T. A.; Nachbar, R. B. Merck Molecular Force Field. IV. Conformational Energies and Geometries for MMFF94. J. Comput. Chem. 1996, 17, 587–615.
6. Chai, J.-D.; Head-Gordon, M. Long-Range Corrected Hybrid Density Functionals with Damped Atom–Atom Dispersion Corrections. Phys. Chem. Chem. Phys. 2008, 10, 6615–6620.
7. Weigend, F.; Ahlrichs, R. Balanced Basis Sets of Split Valence, Triple Zeta Valence and Quadruple Zeta Valence Quality for H to Rn: Design and Assessment of Accuracy. Phys. Chem. Chem. Phys. 2005, 7, 3297–3305.
8. Goerigk, L.; Grimme, S. A Thorough Benchmark of Density Functional Methods for General Main Group Thermochemistry, Kinetics, and Noncovalent Interactions. Phys. Chem. Chem. Phys. 2011, 13, 6670–6688.