Publish. Discover. Accelerate Materials Research.

Publish with confidence

Package curated, citable datasets with DOIs, and rich metadata, all backed by Globus.

Discover DataPublish Data

Datasets published

>880

Published data

>600 TB

ML-ready datasets

>60

Discover

Explore curated datasets tailored to your research.

Browse featured datasets across materials science, simulations, and ML-ready collections.

Browse all datasets
AI/MLFeatured

DFT Estimates of Solvation Energy in Multiple Solvents

130,258 G4MP2 solvation energy calculations for molecules in various solvents.

G4MP2SolvationDFTML-Ready
View dataset
AI/MLFeatured

Dataset for Semantic Segmentation of Dendrites via Machine Learning

Training and test data from in-situ solidification XCT experiment at Argonne National Laboratory Advanced Photon Source.

XCTSegmentationMLDendrites
View dataset

Publish

Share Your Data. Amplify Your Impact.

Make your research citable, discoverable, and accessible to the materials science community with MDF's streamlined publishing workflow.

1

Sign up and Join

Create a free Globus account to get started with MDF publishing.

After registration, join the MDF Globus group to unlock publishing capabilities.

Create Globus AccountJoin MDF Globus Group
2

Prepare Your Data

Organize your data in open, standard formats for maximum accessibility.

Upload from local storage, Globus endpoints, or Google Drive.

Review dataset best practices.

3

Submit for Publishing

Submit your prepared dataset through our guided publishing interface to mint a DOI and make it discoverable.

Publish Your Data
python
1# Import and instantiate Foundry
2from foundry import Foundry
3f = Foundry()
4
5# Now, load the data
6doi = '10.18126/qsdl-qj6x'
7ds = f.get_dataset(doi)
8X,y  = ds.get_as_dict()
9

Foundry-ML

ML-Ready Data. Zero Setup.

Foundry-ML datasets are structured, validated, and ready for machine learning. Load datasets with just a few lines of Python.

1

Import the Foundry library and create an instance

2

Load your dataset using its DOI or MDF identifier

3

Access your data as a structured DataFrame, HDF5, etc

Explore Foundry-ML on GitHub
Spotlight Datasets

Featured Data for High-Impact Research

Curated collections of exceptional quality and scale, designed to accelerate scientific discovery

New Release

OMol25: Electronic Structure Dataset for Molecular Machine Learning

500 terabytes of electronic densities, wavefunctions, and molecular orbital information from over 4 million high-accuracy DFT calculations.

~500 TB Dataset
>4M Calculations
Quantum Chemistry
View All Spotlight Datasets

Impact

MDF by the Numbers

A growing repository of materials science data serving researchers worldwide.

>0Datasets catalogued
>0 TBof published materials data
>0ML-ready datasets

How to Cite

If MDF supports your research, please cite these foundational papers:

Blaiszik, B., K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, and I. Foster. "The Materials Data Facility: Data services to advance materials science research." JOM 68, no. 8, 2016: 2045-2052.

View PaperDOI: 10.1007/s11837-016-2001-3

Blaiszik, Ben, Logan Ward, Marcus Schwarting, Jonathon Gaff, Ryan Chard, Daniel Pike, Kyle Chard, and Ian Foster. "A data ecosystem to support machine learning in materials science." MRS Communications 9, no. 4, 2019: 1125-1133.

View PaperDOI: 10.1557/mrc.2019.118Open Access

Support

The Center for Hierarchical Materials Design (CHiMaD)

CHiMaD is a NIST-sponsored center of excellence for advanced materials research, focusing on developing new tools and methods for materials design and discovery.

Funding Provided by NIST

The Materials Data Facility

MDF development and operations are supported by NIST, enabling the creation of a national data infrastructure for materials science.

Funding Provided by NIST