LeMaterial: an open source initiative to accelerate materials discovery and research
AlexDuvalinho, lritchie, msiron, inelgnu, etiennedufayet, amandinerossello, Ramlaoui, IAMJB, lvwerra, thomwolf
# LeMaterial: an open source initiative to accelerate materials discovery and research

Today, we are thrilled to announce the launch of **LeMaterial**, an open-source collaborative project led by [*Entalpic*](https://entalpic.ai/) and [*Hugging Face*](https://huggingface.co/). LeMaterial aims to simplify and accelerate materials research, making it easier to train ML models, identify novel materials and explore chemical spaces. ⚛️🤗

As a first step, we are releasing a dataset called `LeMat-Bulk`, which unifies, cleans and standardizes the most prominent material datasets, including [Materials Project](https://next-gen.materialsproject.org/), [Alexandria](https://alexandria.icams.rub.de/) and [OQMD](https://oqmd.org/) — giving rise to a single harmonized data format with **6.7M entries** and **7 materials properties.**

> LeMaterial is standing on the shoulders of giants and we are building upon incredible projects which have been instrumental in the development of this initiative: [Optimade](https://www.optimade.org/), [Materials Project](https://next-gen.materialsproject.org/), [Alexandria](https://alexandria.icams.rub.de/) and [OQMD](https://oqmd.org/), and more to come. Please credit them accordingly when using LeMaterial. 

## Why LeMaterial?

The world of materials science, at the intersection of quantum chemistry and machine learning, is brimming with opportunity — from brighter LEDs, to electro-chemical batteries, more efficient photovoltaic cells and recyclable plastics, the applications are endless. By leveraging machine learning (ML) on large, structured datasets, researchers can perform high-throughput screening and testing of new materials at unprecedented scales, significantly accelerating the discovery cycle of novel compounds with desired properties. In this paradigm, **data becomes the essential fuel powering ML models** that can guide experiments, reduce costs, and unlock breakthroughs faster than ever before.

This field is fueled by very complete datasets such as [Materials Project](https://next-gen.materialsproject.org/), [Alexandria](https://alexandria.icams.rub.de/) and [OQMD](https://oqmd.org/), all open-source and under a CC-BY-4.0 license. **However, those datasets vary in format, parameters, and scope, presenting the following challenges:**  

- Dataset integration issues (eg. inconsistent formats or field definitions, incompatible calculations)
- Biases in dataset composition (for eg. Materials Project's focus on oxides and battery materials)
- Limited scope (eg. NOMADs focus on quantum chemistry calculations rather than material properties)
- Lack of clear connections or identifiers between similar materials across different databases

This fragmented landscape makes it challenging for researchers in AI4Science and materials informatics to leverage existing data effectively. Whether the application involves training foundational ML models, constructing accurate phase diagrams, identifying novel materials or exploring the chemical space effectively, there is no simple solution. While efforts like [Optimade](https://optimade.org/) standardize structural data, they don't address discrepancies in material properties or biases in dataset scopes.

**LeMaterial** addresses these challenges by unifying and standardizing data from three major databases — [Materials Project](https://next-gen.materialsproject.org/), [Alexandria](https://alexandria.icams.rub.de/) and [OQMD](https://oqmd.org/) — into a high-quality resource with consistent and systematic properties. The elemental composition treemap below highlights the value of this integration, showcasing how we increase the scope of existing datasets, like Materials Project, which are focused on specific material types, such as battery materials (Li, O, P) or oxides.

<p align="center">
    <img src="https://huggingface.co/datasets/LeMaterial/admin/resolve/main/MP_LeMatBulk_Combined_Treemap.png" alt="drawing" width="1000"/>
</p>

<p align="center">
  <em>Materials Project and LeMat-BulkUnique treemap</em>
</p>


## Achieving a clean, unified and standardized dataset

`LeMat-Bulk` is more than a large-scale merged dataset with a permissive license (CC-BY-4.0). With its 6.7M entries with consistent properties, it represents a foundational step towards creating a curated and standardized open ecosystem for material science, designed to simplify research workflows and improve data quality. Below is a closer view of what is looks like. To interactively browse through our materials, check out the [Materials Explorer space, built using MP Dash components.](https://huggingface.co/spaces/LeMaterial/materials_explorer)

<iframe
  src="https://huggingface.co/datasets/LeMaterial/LeMat-Bulk/embed/viewer/compatible_pbe/train"
  frameborder="0"
  width="100%"
  height="560px"
></iframe>

*[View the complete Datacard](https://huggingface.co/datasets/LeMaterial/LeMat-Bulk#download-and-use-within-python)*


| **Release** | **Description & Value** | **Date** |
|