# LeMaterial: an open source initiative to accelerate materials discovery and research

DevFeed: [LeMaterial: an open source initiative to accelerate materials discovery and research](<https://devfeed.tech/articles/lematerial-an-open-source-initiative-to-accelerate-materials-discovery-and-research-7323.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/lematerial>)

Author: Alexandre Duval; Lucile Ritchie; Martin Siron; Inel DJAFAR; Etienne du Fayet; Amandine Rossello; Ali Ramlaoui; JB D.; Leandro von Werra; Thomas Wolf

Published: 2024-12-10T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Open Source](<https://devfeed.tech/topics/open-source.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [chemistry](<https://devfeed.tech/tags/chemistry.md>), [community](<https://devfeed.tech/tags/community.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [projects](<https://devfeed.tech/tags/projects.md>), [research](<https://devfeed.tech/tags/research.md>), [science](<https://devfeed.tech/tags/science.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

LeMaterial is an open-source collaborative initiative led by Entalpic and Hugging Face to accelerate materials research with machine learning. Its initial release provides a harmonized dataset with 6.7 million entries and seven materials properties, combining prominent materials datasets to support materials discovery, chemical-space exploration, and high-throughput research.

## Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.