---
title: Materials Project Trajectory Dataset
url: https://www.emergentmind.com/topics/materials-project-trajectory-mptrj-dataset
type: topic
---

# Materials Project Trajectory Dataset

The Materials Project Trajectory (MPtrj) dataset, as integrated and distributed within the LeMat-Traj corpus, comprises a harmonized, high-quality collection of density functional theory (DFT) geometry optimization trajectories sourced from the Materials Project. It facilitates both large-scale training of machine learning interatomic potentials (MLIPs) and the benchmarking and fine-tuning of models for atomistic modeling tasks, with rigorous standards in data formatting, functional coverage, and metadata completeness [2508.20875].

## 1. Dataset Scope and Composition

The MPtrj dataset aggregates all full geometry-optimization trajectories published by the Materials Project under CC-BY-4.0. The subset as included in LeMat-Traj is stratified by exchange-correlation functional, capturing a wide range of crystal chemistries. The table below summarizes its size and breakdown by functional:

| Functional | Trajectories | Atomic Frames     |
|:----------:|:------------:|:----------------:|
| PBE        | 195,721      | 3,649,785        |
| PBESol     | 39,981       | 309,873          |
| SCAN       | 7,756        | 180,528          |
| r2SCAN     | 37,888       | 516,576          |

Trajectories are grouped by chemical formula, and fields such as “chemical_formula” and “elements” permit stratification (e.g., by oxide, battery, or intermetallic classes). Notably, MPtrj covers a high representation of oxides (TiO₂, LiFePO₄, etc.) and battery-relevant compounds, providing a chemical counterweight to the bimetallic prevalence in other datasets.

## 2. DFT Calculation Parameters

All MPtrj trajectories originate from full crystal relaxations performed with DFT and harmonize four major exchange-correlation functionals: PBE, PBESol, SCAN, and r2SCAN. Computational parameters are standardized following the Materials Project API: norm-conserving pseudopotentials, Γ-centered k-point meshes (~25 k-points per Å⁻¹), and a plane-wave cutoff near 520 eV. Rigorous convergence filtering is enforced via:

- Energy difference between final and penultimate step ΔE ≤ 2×10⁻² eV.
- Maximum atomic force at the final step ∥F∥∞ ≤ 0.2 eV/Å.

These thresholds produce trajectories that are “reasonably converged,” yet retain moderate-force frames critical for training force-sensitive MLIPs.

## 3. Data Representation and Metadata

Dataset entries adhere to an extended OPTIMADE/JSON schema and are distributed as HuggingFace datasets in a JSON-Lines format, supporting interoperability and efficient downstream usage. Each atomic configuration record encodes:

- atomic_numbers : $[Z_1, Z_2, \ldots]$
- atomic_positions (Å) : $[\mathbf{r}_1, \mathbf{r}_2, \ldots]$
- cell (Å) : $3 \times 3$ lattice matrix
- energy (eV) : total DFT energy
- forces (eV/Å) : $[F_1, F_2, \ldots]$, with $F_i = -\nabla_{\mathbf{r}_i} E[\{\mathbf{r}_j\}]$
- relaxation_step (int) : step in trajectory
- relaxation_number (int) : coarse/fine re-relaxation index
- functional (str) : "PBE", "PBESol", "SCAN", or "r2SCAN"
- source (str) : "MaterialsProject"
- task_id or trajectory_id (str) : original run identifier

Energies may be recast per-atom, $E_{\rm atom} = E/N$, to facilitate normalization and analysis across varying cell sizes.

## 4. Quality Filtering and Harmonization

Quality control is multi-layered:

- Frames missing energy or force data are excluded.
- Trajectories with ΔE(final–penultimate) > 2×10⁻² eV are discarded.
- Trajectories with final step ∥F∥∞ > 0.2 eV/Å are discarded.
- All entries are validated against the OPTIMADE schema.

Harmonization ensures all energies (eV), forces (eV/Å), and distances (Å) are consistent across the dataset. Frames are grouped by functional, permitting training of either functional-specific or multi-fidelity MLIP models.

## 5. Programmatic Access and Data Layout

MPtrj is accessible via direct HuggingFace datasets and through the LeMaterial-Fetcher Python API. Loading the Materials Project PBE subset, for instance, employs:

```python
from lemater ial_fetcher import LeMatFetcher
fetcher = LeMatFetcher()
mptrj_pbe = fetcher.load_split(source="MaterialsProject", functional="PBE")
# mptrj_pbe is now a HuggingFace Dataset with columns as above
```

Data organization follows a modular directory structure with Apache Arrow columnar files:

```
LeMat-Traj/
 ├── PBE/
 │    ├── mp_pbe.arrow
 │    ├── alexandria_pbe.arrow
 │    └── oqmd_pbe.arrow
 ├── PBESol/
 ├── SCAN/
 └── r2SCAN/
```

This enables efficient memory-mapped I/O for large-scale processing.

## 6. Practical Integration and Performance Benchmarks

MPtrj facilitates several atomistic modeling workflows:

- On Matbench Discovery benchmarks, a graph neural network (GNN) potential trained solely on MPtrj (PBE) achieves an F1 score ≃ 0.694, compared to 0.575 for high-force-only datasets like OMat24.
- Fine-tuning an OMat24-pretrained model on MPtrj/PBE increases stability-prediction F1 to ≃ 0.772, demonstrating its utility for near-equilibrium refinement.
- A recommended training workflow involves initial pre-training on a broad high-force molecular dynamics or active-learning dataset, followed by fine-tuning on MPtrj, to optimize low-force performance in geometry optimization.

For self-supervised learning, the “relaxation_step” field may be exploited for contrastive or masked-reconstruction objectives along optimization paths.

## 7. Significance and Recommended Practice

The MPtrj subset within LeMat-Traj delivers nearly 5 million converged, low-to-moderate force crystal frames, harmonized in a fully OPTIMADE-compliant schema across four DFT functionals. This design supports both functional-specific and multi-fidelity MLIP development, enabling:

- Fine-tuning of pre-trained models for relaxation tasks.
- Exploration of self-supervised and amortized-optimization algorithms.
- Efficient, large-scale reproducible experimentation leveraging standardized APIs and data representation [2508.20875].

*A plausible implication is that the harmonized nature and broad functional coverage of MPtrj will facilitate cross-comparative studies of MLIP generalization and transferability across chemical and functional spaces.*

Source: https://www.emergentmind.com/topics/materials-project-trajectory-mptrj-dataset