# Molecular Geometry Optimization

> Use when you have generated multiple 3D conformers (e.g., from RDKit's distance-geometry algorithm) of ionized adducts and need to relax them toward local minima before filtering with machine-learning potentials or quantum methods.

- Skill: `holobiomicslab/molecular-geometry-optimization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/molecular-geometry-optimization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/molecular-geometry-optimization/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/molecular-geometry-optimization

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# molecular-geometry-optimization

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Optimize 3D molecular geometries toward local energy minima using force-field methods, preparing conformer ensembles for downstream quantum or machine-learning filtering. This skill bridges conformer generation and high-level validation by relaxing structures to physically plausible states.

## When to use

Apply this skill when you have generated multiple 3D conformers (e.g., from RDKit's distance-geometry algorithm) of ionized adducts and need to relax them toward local minima before filtering with machine-learning potentials or quantum methods. Use it to eliminate high-strain, unphysical geometries that would waste computational resources in expensive downstream steps.

## When NOT to use

- Input is a single rigid crystal structure or experimentally resolved conformation—optimization may introduce unnecessary perturbation.
- Computational budget does not permit even fast force-field minimization; consider skipping to machine-learning filtering if conformer count is very large (>10,000 per molecule).
- Conformers are already validated by a higher-accuracy method (e.g., already filtered by machine learning)—re-optimization is redundant.

## Inputs

- Ensemble of 3D conformers with hydrogens attached (SDF or MOL format)
- Ionized adduct structures from upstream ionization-state determination
- Conformer count specification (e.g., 50–500 samples per molecule)

## Outputs

- Relaxed 3D conformer ensemble with minimized geometries (SDF or pickle format)
- Conformer ensemble retained for downstream ASE-ANI filtering

## How to apply

For each conformer in the ensemble, apply a force-field geometry minimization using either MMFF94 or UFF. Run minimization until convergence (typically gradient-based optimization to a tolerance threshold). Retain all minimized conformers in an ensemble format (SDF or pickle) for export to downstream ASE-ANI filtering. The rationale is that force-field relaxation is fast (compared to quantum or neural-network evaluations), removes obvious strain, and ensures the conformer ensemble is physically initialized for more expensive filtering and quantum calculations.

## Related tools

- **RDKit** (Generate initial 3D coordinates and conformers; supply force-field minimization routines (MMFF94 or UFF)) — https://www.rdkit.org
- **ASE-ANI** (Downstream filtering of relaxed conformer ensemble using neural-network potential) — https://github.com/isayev/ASE_ANI
- **Snakemake** (Workflow orchestration for parallelized geometry optimization across multiple molecules) — https://github.com/DasSusanta/snakemake_ccs

## Examples

```
from rdkit import Chem; from rdkit.Chem import AllChem; mol = Chem.AddHs(Chem.MolFromSmiles('[NH4+]')); AllChem.EmbedMolecule(mol); AllChem.UFFGetMoleculeForceField(mol).Minimize(); Chem.SDWriter('optimized.sdf').write(mol)
```

## Evaluation signals

- All conformers converge to a local minimum (energy gradient below convergence tolerance); check final RMS gradient reported by minimizer.
- No conformer remains in a high-energy state (outlier energies >3σ above median suggest incomplete optimization or strain).
- Geometry ensemble diversity is preserved: inter-conformer RMSD values remain consistent before and after minimization, confirming relaxation did not collapse distinct conformations.
- Downstream ASE-ANI filtering step accepts all conformers without crashing; no invalid molecular structures (e.g., overlapping atoms, broken bonds).
- Output ensemble file size and conformer count match input (SDF or pickle format is readable and complete).

## Limitations

- Force-field methods (MMFF94, UFF) may not accurately describe highly strained or unusual functional groups; ASE-ANI filtering is essential before quantum calculation.
- ASE-ANI README notes the tool is DEPRECATED and recommends migration to TorchANI; downstream filtering infrastructure may require updates.
- Local minima from force-field optimization may differ from those found by higher-accuracy quantum methods; this is acceptable as a rapid filter, but some conformers may be discarded that would survive quantum screening.
- Very large conformer ensembles (>1000 per molecule) incur cumulative computational cost even with fast force fields; practical limits depend on HPC walltime allocation.

## Evidence

- [other] Use RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima: "Minimize each conformer's geometry using RDKit's built-in force field (MMFF94 or UFF) to relax structures toward local minima."
- [other] Conformer ensemble exported in SDF/pickle for downstream filtering: "Export the conformer ensemble to SDF or pickle format with all conformers retained for downstream filtering."
- [readme] Force-field minimization is fast and prepares conformers for neural-network filtering: "ASE-ANI: For conformation filtering. Available at: [https://github.com/isayev/ASE_ANI]"
- [other] Workflow context: ionized structures → conformer generation → force-field optimization → ASE-ANI filtering: "RDKit is used in the workflow as the conformation generation tool that operates on ionized adduct structures produced by Dimorphite-DL, generating 3D conformers that are subsequently filtered by"