Molecular Conformer Search & Ranking

Goal

Generate a diverse ensemble of low-energy conformers for a given molecule. The workflow combines:

1. Stochastic sampling using RDKit's ETKDG algorithm (Experimental Torsion Distance Geometry).
2. High-accuracy relaxation using Machine Learning Interatomic Potentials (MLIPs) to get near-DFT quality geometries and energies.
3. Deduplication and Boltzmann weighting to identify the most relevant conformers at finite temperature.

[!IMPORTANT] This skill is optimized for organic molecules and uses MACE-OFF23 models by default. For inorganic clusters, switch to MACE-OMAT or MatGL models.

Recommended Models

• MACE-OFF23: MACE-OFF23-small (default), MACE-OFF23-medium — trained on organic molecules (Env: mace-agent)
• MACE-MH: MACE-MH-1 with head omol — multi-head model with molecular head (Env: mace-agent)
• UMA: uma-s-1p1 with head omol — general molecular model (Env: fairchem-agent)

1. Prerequisites