---
title: 'Open Molecules 2025 (OMol25): Quantum-Chem Data'
url: https://www.emergentmind.com/topics/open-molecules-2025-omol25
type: topic
---

# Open Molecules 2025 (OMol25): Quantum-Chem Data

Open Molecules 2025 (OMol25) is a foundational quantum-chemical dataset specifically constructed to advance machine-learning interatomic potentials (MLIPs) and universal molecular AI. With its unprecedented scale, chemical diversity, and high-fidelity density functional theory (DFT) computations, OMol25 forms the backbone for both large-scale molecular modeling benchmarking and integrative research in computational chemistry, molecular simulation, and generative molecular understanding [2505.08762, 2510.00027, 2502.01074].

## 1. Origins, Motivations, and Design Goals

OMol25 originated from the need for a dataset that simultaneously addresses (1) the lack of comprehensive, high-accuracy, open-access DFT datasets spanning the entire periodic table (through Bismuth, Z=83); (2) systematic support for data- and model-scaling studies; and (3) robust benchmarking of MLIPs, including out-of-distribution (OOD) generalization [2510.00027, 2505.08762]. 

Key design objectives included:
- Generating millions of unique molecular configurations with energies and forces computed at a single, consistent DFT level (ωB97M-V/def2-TZVPD) to avoid bias from methodological heterogeneity.
- Covering a broad swath of chemical domains, including small organics, biomolecules, electrolytes, inorganic/organometallic complexes, and reactivity intermediates.
- Providing standardized data splits for both in-distribution and OOD evaluation, with domain-specific subsets to enable targeted, reproducible benchmarking.
- Ensuring data are accessible and broadly reusable through open-source provision and a unified API (FAIRChem, version 2.2.0).

## 2. Dataset Composition, Scale, and Organization

The OMol25 dataset comprises approximately 100 million DFT single-point calculations, representing about 6 billion CPU-core hours aimed at maximizing chemical, structural, and elemental diversity [2505.08762]. The dataset contains approximately 83 million unique systems, spanning molecular sizes from diatomics to 350-atom complexes, with coverage summarized by domain:

| Domain            | Snapshots (N=100M) | Atom-count share | Unique systems (%) |
|-------------------|-------------------:|-----------------:|-------------------:|
| Biomolecules      | 18,000,000         | 19%              | 12%                |
| Metal complexes   | 19,000,000         | 19%              | 14%                |
| Community organics| 22,000,000         | 22%              | 20%                |
| Electrolytes      | 30,000,000         | 30%              | 25%                |
| Reactivity/Other  | 11,000,000         | 10%              | 29%                |

Domain assignments reflect broad chemical basis: OMol25 includes not only small molecules and biomolecular fragments but also explicit solvation, spin/charge variation (charges −10 to +10, spins 1–11), and a large body of reactive structures (e.g., transition-state–like geometries from AFIR, geodesics, Popcornn).

- **Elemental Coverage**: Every element Z=1–83 appears in ≥10,000 structures; typical heavy-atom coverage per element spans 10⁵–10⁸ snapshots.
- **Conformational and Reactive Sampling**: Classical and ML-based MD, CREST, ESMACS, AFIR, and other protocols ensure coverage of equilibrium, off-equilibrium, and highly reactive configurations [2505.08762].

## 3. Data Generation, Preprocessing, and Statistical Characterization

All DFT computations use the ωB97M-V functional with def2-TZVPD basis in ORCA 6.0, with RI-J and COSX acceleration, tight convergence, and GRID3 settings. This protocol provides consistent, energy–force-consistent reference data for MLIP development.

Each configuration includes:
- Centered atomic coordinates (by the molecular center of mass).
- Atomic numbers, total molecular charge $q \in \mathbb{Z}$, and spin multiplicity $s \in \mathbb{N}$.
- Per-configuration energy $E$ (eV or eV/atom) and atom-wise forces $\mathbf{F} \in \mathbb{R}^{|m|\times3}$ (eV/Å).
- Index files with SMILES, InChI keys, and split assignments.

Data diversity is characterized by:
- Bond-order (single to triple, dative/metal–ligand).
- Coordination numbers (metals: 2–12).
- Conformer-energy ranges (up to several eV above minima).
- Statistical metrics for elemental, charge, spin, and size distributions can be computed by histograms or through embedding-space coverage, where for region $A \subset \mathbb{R}^d$,

$$
\mathrm{Coverage}(A) = \frac{|\{\,m\in\mathrm{train}:f(m)\in A\}|}{|\mathrm{train}|}
$$

## 4. Data Access, Splits, and Benchmarking Protocols

OMol25 is openly distributed via Zenodo and GitHub, formatted in HDF5 or sharded NPZ archives, with per-molecule arrays and comprehensive index files. The FAIRChem package provides canonical scripts and loaders for standardized training and evaluation [2510.00027].

**Canonical splits:**
- Training split (“4M split”): ~3.99 million samples.
- Validation split (Val-Comp): ~2.76 million OOD configurations.
- Domain-specific validation sets: metal complexes, electrolytes, biomolecules, neutral organics (e.g., ANI-2X, OrbNet-Denali, GEOM), SPICE, reactivity (typically ~20k samples each).
- Additional test splits for leaderboard evaluations.

**Benchmarking support:**
Evaluation metrics are standardized via FAIRChem and include:
- Force MAE (eV/Å): $ \frac{1}{3|m|} \sum_{i=1}^{|m|} \sum_{\alpha\in\{x,y,z\}} |\!F_{i,\alpha}-F^*_{i,\alpha}| $
- Force cosine similarity: $ \frac{1}{|m|} \sum_{i=1}^{|m|} \frac{\mathbf{F}_i \cdot \mathbf{F}^*_i}{\|\mathbf{F}_i\|\|\mathbf{F}^*_i\|} $
- Energy per atom MAE (eV/atom): $ \frac{1}{|m|}|E - E^*| $
- Total energy MAE (eV): $ |E - E^*| $

Seven additional downstream tasks are defined: protein–ligand interaction, ligand strain, conformer ranking, protonation energies, unoptimized IE/EA and spin gaps, distance-scaling noncovalent interactions, and strained conformers (Wiggle150) [2505.08762].

## 5. Baseline MLIP Models and Evaluations

OMol25 provides baseline models, including three versions of Equivariant Spectral ENcoder (eSEN), GemNet-OC, and MACE (on the neutral split) [2505.08762]:

- **eSEN**: E(3)-equivariant Transformer using a learned basis with variable $l_{max}$, nodewise normalization, and gated nonlinearity.
- **GemNet-OC**: Invariant message-passing GNN using atom/edge/triplet/quad features and spherical harmonics.
- **MACE**: High-order equivariant message passing for explicit three- and four-body interactions.

Key results on the "All" split:

| Model            | Energy MAE (meV/atom) | Force MAE (meV/Å) | PL Energy (meV) | IE/EA ΔE (meV) | SR ΔE (meV) | LR ΔE (meV) |
|------------------|----------------------:|------------------:|----------------:|--------------:|------------:|------------:|
| eSEN-sm-cons.    | 1.35                  | 7.39              | 147.3           | 315           | 28.6        | 268         |
| GemNet-OC        | 0.57                  | 5.85              | 19.4            | 254           | 11.8        | 143         |

Observed limitations include persistent large errors for charge/spin-dependent properties and long-range dissociation, motivating future architectural innovations.

## 6. Integration with Molecular AI and Universal Models

OMol25 serves as both a training corpus and robust benchmark for universal molecular AI frameworks, such as Omni-Mol [2502.01074]. These systems leverage OMol25’s scale and chemical diversity to develop:

- Unified encoding mechanisms that integrate natural-language instructions, SELFIES strings, and molecular graphs via a single encoder.
- Active-learning-based data selection to sample the most informative data (Omni-Mol uses a 40% subset selected iteratively for maximal informativeness).
- Advanced stabilization strategies (e.g., adaptive LoRA scaling, anchor-and-reconcile experts in MoE architectures) to manage gradient variance and inter-task conflict.
- Instruction-tuned, multi-task architectures achieving state-of-the-art performance on reaction, regression, description, and action tasks.
- Characterization of scaling laws: average performance fits $y = 0.07 \cdot \log(x) + 0.41$ (data scaling), and $y = 0.02 \cdot \log(x) + 0.32$ (model scaling), with $R^2$ near 0.98, indicating robust generalization as model/data scale increases.

The convergence of task spaces and unified representations supports OMol25’s goal of establishing a common platform for integrative molecular AI.

## 7. Applications, Impact, and Future Directions

OMol25 enables:
- Large-scale and systematic scaling studies for MLIP architectures;
- Cross-domain, cross-architecture benchmarking under standardized protocols;
- Empirical analysis of symmetry-enforcing strategies (e.g., rotational augmentation vs. learned equivariance [2510.00027]);
- Accelerated high-throughput molecular screening, ML-accelerated ab initio MD, and property prediction across organics, biomolecules, metal complexes, and electrolytes;
- Development of charge/spin- and polarization-aware force fields for batteries, catalysis, drug discovery, and more.

Planned and suggested extensions include coverage of actinides ($Z>83$), multimetallic clusters, computation of Hessians, spectroscopic observables, free-energy workflows, and expansion to new domains such as radical cascades and generative molecular design [2505.08762]. OMol25 is positioned as an open, scalable resource for academic and industrial research communities aiming to construct the next generation of AI-driven molecular models and simulations.

Source: https://www.emergentmind.com/topics/open-molecules-2025-omol25