Cost-Effective Accuracy in Molecular Structures via Smart Databases, Topological Features, and Random Forests.

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Organic Chemistry

Background:

• Accurate prediction of molecular geometries is crucial for understanding chemical properties and designing new molecules.
• Existing methods can be computationally expensive or lack sufficient accuracy for complex organic systems.

Purpose of the Study:

• To present a cost-effective and accurate protocol for predicting equilibrium geometries of organic molecules.
• To develop a robust workflow applicable to hydrocarbons and heteroatom-containing compounds.

Main Methods:

• Utilizing chemically grounded descriptors and atom synthons for representative structure selection from the QM9 database.
• Employing a two-tier protocol involving feature-based selection and Random Forest regression for bond-length corrections.
• Reoptimizing selected structures with a composite electronic-structure scheme.

Main Results:

• Achieved near-spectroscopic accuracy in predicted geometries for a wide range of organic molecules.
• Demonstrated excellent performance across hydrocarbons, heteroatom-containing species, and sterically congested frameworks.
• Validated the protocol against high-level quantum mechanical and semiexperimental data.

Conclusions:

• The developed protocol offers a balance between computational efficiency and high structural fidelity.
• The automated workflow ensures reproducibility and facilitates applications in thermochemistry, spectroscopy, and force-field parametrization.
• Atom synthons provide a powerful, topology-aware representation for chemical structure analysis.