README.md

---
license: cc-by-4.0
task_categories:
  - other
tags:
  - chemistry
  - molecular-dynamics
  - machine-learning-force-fields
  - mlff
  - benchmarking
  - compositional-generalisation
  - gnn
  - equivariant
  - ab-initio
pretty_name: GMD-26
size_categories:
  - 100K<n<1M
---

# GMD-26: Benchmarking Compositional Generalisation for Machine Learning Force Fields

GMD-26 is a systematic benchmark dataset for evaluating the **compositional generalisation** capabilities of Machine Learning Force Fields (MLFFs). Unlike standard benchmarks that train and test on the same molecular systems, GMD-26 explicitly separates training and test molecules, probing whether models have learned transferable physical principles or merely interpolate within their training distribution.

> **Paper:** *Benchmarking Compositional Generalisation for Learning Inter-atomic Potentials* (submitted to NeurIPS 2026)

---

## Dataset Overview

GMD-26 covers **118 molecules** across ten fragment groups based on substituted linear alkyl carbon chains, yielding **over 296,000 labelled geometries**. Each molecule has two AIMD trajectories:

- **Primary trajectory:** ~2,013 snapshots — used for training and OOD test splits
- **Secondary trajectory:** 500 snapshots — used for in-distribution (ID) test splits (first 100 for hyperparameter tuning, remaining 400 for ID evaluation)

Energies and forces are labelled at the **PBE/def2-TZVP + D3BJ** level of theory using ORCA, making this dataset directly compatible with standard MLFF training pipelines.

---

## Molecular Families

| Functional Group | Example Molecules |
|---|---|
| Alkanes | Ethane – Tridecane |
| Primary Alcohols | Ethanol – Pentadecan-1-ol |
| Aldehydes | Ethanal – Pentadecanal |
| Carboxylic Acids | Ethanoic acid – Pentadecanoic acid |
| Primary Amines | Ethanamine – Octan-1-amine |
| Primary Amides | Butanamide – Octanamide |
| Diamines | Ethane-1,2-diamine – Decane-1,10-diamine |
| Dicarboxylic Acids | Ethanedioic acid – Hexadecanedioic acid |
| Amino Acids | 2-Aminoethanoic acid – 10-Aminodecanoic acid |
| Complex Multifunctional | Various diols, triols, tetraols, diones, dialdehydes |

---

## Benchmark Tasks

GMD-26 defines four tasks that each require a distinct form of compositional generalisation. In all tasks, training and OOD test molecules are disjoint.

### Task 1 — Fragment Chain Extension
Tests whether a model can extrapolate to **longer carbon chains** than seen during training.

- **Base:** Train on alkanes C2–C6; test on alkanes C7–C13
- **Augmented:** Train on short/long alcohols and medium carboxylic acids; test on the complementary length ranges of each family

### Task 2 — Fragment Composition
Tests whether a model can generalise to a **novel functional group** that is a chemical composition of functional groups seen individually during training.

- **Base:** Train on alcohols and aldehydes; test on carboxylic acids (–COOH ≈ –OH + –CHO)
- **Augmented:** Training additionally includes amines and amides, providing an explicit demonstration of functional group composition

### Task 3 — Fragment Duplication
Tests whether a model can generalise from a molecule with **one occurrence** of a functional group to the same molecule with **two occurrences**.

- Train on monocarboxylic acids (C5–C10); test on the corresponding dicarboxylic acids of identical chain lengths

### Task 4 — Fragment Combination
Tests whether a model can generalise to **asymmetrically functionalised** molecules when trained exclusively on symmetrically functionalised analogues.

- Train on diamines and dicarboxylic acids (C2–C9); test on the corresponding amino acids (one amine + one carboxylic acid)

### Task Splits at a Glance

| Task | Training Set | OOD Test Set |
|---|---|---|
| 1 Base | C2–C6 alkanes | C7–C13 alkanes |
| 1 Augmented | C2–C3 & C9–C15 alcohols; C4–C8 carboxylic acids | C4–C8 alcohols; C2–C3 & C9–C15 carboxylic acids |
| 2 Base | C4–C10 alcohols, aldehydes; C7–C11 complex carbonyls/alcohols | C4–C10 carboxylic acids |
| 2 Augmented | As above + C4–C10 amines and amides | C4–C10 carboxylic acids |
| 3 | C5–C10 monocarboxylic acids | C5–C10 dicarboxylic acids |
| 4 | C2–C9 diamines; C2–C9 dicarboxylic acids | C2–C9 amino acids |

---

## Generation Pipeline

Each trajectory was generated via a four-stage pipeline:

1. **Conformer generation** — RDKit generates an initial 3D geometry from a SMILES string
2. **Trajectory sampling** — FlashMD (PET-MAD / OMat-PES checkpoint) propagates molecular dynamics in vacuum for 2×10⁵ steps at 16 fs timestep, Langevin thermostat at 300 K, recording every 100 steps (~2,000 snapshots)
3. **DFT relabelling** — Each snapshot is relabelled at PBE/def2-TZVP + D3BJ using ORCA (`!EnGrad PBE D3BJ def2-TZVP RI TightSCF`); snapshots where SCF convergence fails are excluded
4. **Packaging** — Outputs stored in extended XYZ format (compatible with ASE) alongside pre-computed task splits

---

## Data Format

Data is stored in **ASE trajectory format** (`.traj`), readable natively with the Atomic Simulation Environment. Each frame contains:

| Field | Description |
|---|---|
| `positions` | Atomic coordinates (Å) |
| `numbers` | Atomic numbers |
| `energy` | Total PBE/def2-TZVP+D3BJ energy (eV) |
| `forces` | Per-atom force vectors (eV/Å) |

Pre-computed split files for all four tasks are included so that reproducing the evaluation protocol does not require re-running any upstream generation stages.

---

## Loading the Dataset

```python
# Using huggingface_hub (raw files)
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="AmirMasoud/GMD-26",
    repo_type="dataset",
    local_dir="./GMD-26"
)
```

```python
# Using ASE to read individual trajectory files
from ase.io.trajectory import Trajectory

traj = Trajectory("path/to/molecule.traj")
energies = [atoms.get_potential_energy() for atoms in traj]
forces   = [atoms.get_forces() for atoms in traj]
```

---

## GOAL Training Framework

Alongside the dataset we release **GOAL** (available upon paper acceptance), a PyTorch Lightning-based framework that:

- Exposes GMD-26 as a benchmark module with a unified data-loading interface
- Supports from-scratch training of arbitrary MLFF architectures via a single `forward(graph) -> dict` method
- Wraps foundation models (MACE-MP-0 small/medium/large, UMA-small) through thin adapters for fine-tuning under identical conditions
- Supports DDP, FSDP, and FSDP2 for multi-GPU and multi-node training (tested on NVIDIA GH200)
- Configures all experiments through Hydra for full reproducibility
- Exposes trained models as ASE calculators for downstream MD simulation

### Extending the Benchmark

New molecular families can be added by providing SMILES strings and a YAML task specification:

```yaml
task: chain_extension
fragment: alcohol
train_lengths: [2, 3, 4, 5, 6]
ood_lengths: [7, 8, 9, 10, 11, 12, 13]
n_snapshots_per_trajectory: 1000
```

The four task templates are defined over abstract fragment sets and reusable without modification.

---

## Citation

If you use GMD-26 in your research, please cite:

```bibtex
@inproceedings{nourollah2026gmd26,
  title     = {Benchmarking Compositional Generalisation for Learning Inter-atomic Potentials},
  author    = {Nourollah, Amir Masoud and Khalid, Irtaza and Leoni, Stefano and Schockaert, Steven},
  booktitle = {The Fourteenth International Conference on Learning Representations (ICLR 2026)},
  year      = {2026},
  url       = {https://openreview.net/forum?id=WxTlAbRUE6}
}
```

> **Note:** The paper is currently under review. If accepted, the citation will be updated with the final publication details.

---

## Licence

This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) licence.