RESEARCH
CORE MACHINE LEARNING
Open Molecular Crystals 2025 (OMC25) Dataset and Models
August 05, 2025
Abstract
The development of accurate and efficient machine learning models for predicting the structure and properties of molecular crystals has been hindered by the scarcity of publicly available datasets of structures with property labels. To address this challenge, we introduce the Open Molecular Crystals 2025 (OMC25) dataset, a collection of over 27 million molecular crystal structures containing 12 elements and up to 300 atoms in the unit cell. The dataset was generated from dispersion-inclusive density functional theory (DFT) relaxation trajectories of over 230,000 randomly generated molecular crystal structures of around 50,000 organic molecules. OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide detailed information on the dataset's construction, composition, structure, and properties. To demonstrate the quality and use cases of OMC25, we further trained and evaluated state-of-the-art open-source machine learning interatomic potentials. By making this dataset publicly available, we aim to accelerate the development of more accurate and efficient machine learning models for molecular crystals.
AUTHORS
Written by
Yi Yang
Xiang Fu
Haoran Ni
Matt Uyttendaele
Noa Marom
Xingyu Liu
Anuroop Sriram
Arman Boromand
Brandon M. Wood
Daniel S. Levine
Keian Noori
Kyle Michel
C. Lawrence Zitnick
Luis Barroso-Luque
Misko Dzamba
Muhammed Shuaibi
Meng Gao
Vahe Gharakhanyan
Zachary W. Ulissi
Publisher
arXiv
Research Topics
Core Machine Learning
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