# Qm9 Property Prediction Eval

> Assesses a model's ability to predict scalar quantum chemical properties from molecular structures. It evaluates accuracy on five key electronic and vibrational targets using mean absolute error and average ranking. Use when the user wants to benchmark on QM9, or asks about evaluating this task. Reports MAE.

- Skill: `qhjqhj00/qm9-property-prediction-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/qm9-property-prediction-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/qm9-property-prediction-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/qm9-property-prediction-eval

---


# qm9-property-prediction-eval

> Molecular Representations in Implicit Functional Space via Hyper-Networks — Wang et al. (2026) (arXiv:2601.22327, 2026)

## What this evaluates

Assesses a model's ability to predict scalar quantum chemical properties from molecular structures. It evaluates accuracy on five key electronic and vibrational targets using mean absolute error and average ranking.

## Datasets

- **QM9** — total 130000; splits: train (100000), val (18000), test (13000)

## Metrics

- `MAE` **(primary)** — range: other
  - Mean Absolute Error between predicted and true scalar values for each of the five properties (HOMO, dipole, R2, polarizability, ZPVE). Lower is better.
- `A.R.` — range: other
  - Average Ranking across all five properties, computed by ranking methods per property and averaging ranks (ties receive averaged ranks). Lower is better.

## Input / output format

**Input**: Molecular graph/structure with atomic coordinates and types.

**Output**: Scalar predictions for HOMO energy, dipole moment, electronic spatial extent, polarizability, and zero-point vibrational energy.

## Scoring recipe

```python
mae = np.mean(np.abs(pred - true))
ranks = [rankdata(-mae_per_property) for property in properties]
ar = np.mean(ranks)
```

## Common pitfalls

- MAE units vary significantly across properties (meV, mD, mα₀², etc.), making cross-property comparison difficult without normalization.
- Average Ranking (A.R.) is computed by ranking methods per property and averaging; ties receive averaged ranks, which can skew results if many methods cluster.

## Evidence (verbatim from paper)

> Five scalar targets are considered: ε_HOMO, μ, R^2, α, and ZPVE, with mean absolute error (MAE) as the evaluation metric.

## Citation

```bibtex
@misc{wang2026molfield,
  title={Molecular Representations in Implicit Functional Space via Hyper-Networks},
  author={Wang et al. (2026)},
  year={2026},
  note={arXiv:2601.22327}
}
```

- arXiv: 2601.22327