ArticleScientific data2026
Open Molecular Crystals 2025 (OMC25) dataset and models.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
12 citing papers in PubMed.
- Accessible hybrid DFT-quality NMR crystallographyChemical science · 2026Article
- MolCryst-MLIPs: A Machine-Learned Interatomic Potentials Database for Molecular Crystals.Journal of chemical theory and computation · 2026Article
- Article
- Quantitative Prediction of Exchangeable Proton Chemical Shifts.Nature communications · 2026Article
- Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability.Journal of the American Chemical Society · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- MXtalTools: A Toolkit for Machine Learning on Molecular Crystals.Journal of chemical information and modeling · 2026Article
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 2026Article
- Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- From polymorphs to cocrystals and salts: successfully predicting axitinib's challenging crystal forms.Chemical science · 2025Article
- Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.Macromolecules · 2025Review
- Genarris 3.0: Generating Close-Packed Molecular Crystal Structures with Rigid Press.Journal of chemical theory and computation · 2025Article
Corrections and comments
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Authors and funding
19 authors.
Funding
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 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 created by relaxing over 230,000 randomly constructed molecular crystal structures-representing approximately 50,000 organic molecules-using dispersion-inclusive density functional theory (DFT) with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional combined with Grimme's D3 dispersion correction (PBE+D3). OMC25 comprises diverse chemical compounds capable of forming different intermolecular interactions and a wide range of crystal packing motifs. We provide information on the dataset's construction, composition, and properties. To demonstrate the quality and use cases of OMC25, we 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 accurate and efficient machine learning models for molecular crystals.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.