Foundation models have shown remarkable success across scientific domains, yet their impact in chemistry remains limited due to the absence of diverse, large-scale, high-quality datasets that reflect the field's multifaceted nature. We present the ChemPile, an open dataset containing over 75 billion tokens of curated chemical data, specifically built for training and evaluating general-purpose models in the chemical sciences. The dataset mirrors the human learning journey through chemistry -- from educational foundations to specialized expertise -- spanning multiple modalities and content types including structured data in diverse chemical representations (SMILES, SELFIES, IUPAC names, InChI, molecular renderings), scientific and educational text, executable code, and chemical images. ChemPile integrates foundational knowledge (textbooks, lecture notes), specialized expertise (scientific articles and language-interfaced data), visual understanding (molecular structures, diagrams), and advanced reasoning (problem-solving traces and code) -- mirroring how human chemists develop expertise through diverse learning materials and experiences. Constructed through hundreds of hours of expert curation, the ChemPile captures both foundational concepts and domain-specific complexity. We provide standardized training, validation, and test splits, enabling robust benchmarking. ChemPile is openly released via HuggingFace with a consistent API, permissive license, and detailed documentation. We hope the ChemPile will serve as a catalyst for chemical AI, enabling the development of the next generation of chemical foundation models.
View on arXiv@article{mirza2025_2505.12534,
title={ ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models },
author={ Adrian Mirza and Nawaf Alampara and Martiño Ríos-García and Mohamed Abdelalim and Jack Butler and Bethany Connolly and Tunca Dogan and Marianna Nezhurina and Bünyamin Şen and Santosh Tirunagari and Mark Worrall and Adamo Young and Philippe Schwaller and Michael Pieler and Kevin Maik Jablonka },
journal={arXiv preprint arXiv:2505.12534},
year={ 2025 }
}