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 and Hugging Face . 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 , Alexandria and OQMD — 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 , Materials Project , Alexandria and OQMD , and more to come. Please credit them accordingly when using LeMaterial.

Current approaches to identifying a material as novel relative to a database have predominantly relied on similarity metrics, which necessitate a combinatorial effort to screen the existing database for novelty. To provide faster novelty detection in a dataset, Entalpic introduces a hashing method to compute the fingerprint of a material.

Below lies a comparison of our hash function with the StructureMatcher of Pymatgen, to find all duplicates of a dataset. The experiment was run on two datasets having very different structures.

This field is fueled by very complete datasets such as Materials Project , Alexandria and OQMD , all open-source and under a CC-BY-4.0 license. However, those datasets vary in format, parameters, and scope, presenting the following challenges:

Compare materials properties across databases and functionals: by providing researchers with data across DFT functionals, and by linking materials via our materials fingerprint algorithm we are able to establish and connect materials properties calculated via different parameters. This gives researchers insight into how functionals might behave and differ across compositional space.

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.

By downloading content from LeMaterial, you agree to accept the Creative Commons Attribution 4.0 license implying that content may be copied, distributed, transmitted, and adapted, without obtaining specific permission from LeMaterial, provided proper attribution is given to LeMaterial.

CC-BY-4.0 (license used for Materials Project, Alexandria, OQMD) requires proper acknowledgement. Thus, if you use materials data which include (”mp-”) in the immutable_id, please cite the Materials Project . If you use materials data which include (”agm-”) in the immutable_id, please cite Alexandria, PBE or Alexandria PBESol, SCAN . If you use materials data which include (”oqmd-”) in the immutable_id, please cite OQMD . Finally, if you make use of the Phase Diagram for visualization purposes, or the crystal viewer in the Materials Explorer, please acknowledge Crystal Toolkit .

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 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 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 , Alexandria and OQMD — 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.