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PO_1138544377395884197036113 | 1138544377395884197036113575438127558724892385132421843175606163985462120998042275566304273432377280496984039202444259570159070917584449561899767092153530 | 2025-08-05T13:32:00 | DS_h8s4lfyits34_0 | 1 | VASP | DFT-PBEsol | -1.953743 | [
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PO_4106567106427074675911336 | 4106567106427074675911336530805298064003529036485039358552281542299315546274224634484602154409737368107715124953694805956400303636666988605014721789667419 | 2025-08-05T13:32:00 | DS_h8s4lfyits34_0 | 1 | VASP | DFT-PBEsol | -16.180648 | [
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[
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Cite this dataset
Mazitov, A., Chorna, S., Fraux, G., Bercx, M., Pizzi, G., De, S., and Ceriotti, M. Massive Atomic Diversity MAD train. ColabFit, 2025. https://doi.org/10.60732/f5b6ea1b
Cite this dataset
Mazitov, A., Chorna, S., Fraux, G., Bercx, M., Pizzi, G., De, S., and Ceriotti, M. Massive Atomic Diversity MAD train. ColabFit, 2025. https://doi.org/10.60732/f5b6ea1bThis dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_h8s4lfyits34_0
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Dataset Name
Massive Atomic Diversity MAD train
Description
The training split of the MAD (Massive Atomic Diversity) dataset. From the creators: Starting from relatively small sets of stable structures, the dataset is built to contain “massive atomic diversity” (MAD) by aggressively distorting these configurations, with near-complete disregard for the stability of the resulting configurations. The electronic structure details, on the other hand, are chosen to maximize consistency rather than to obtain the most accurate prediction fora given structure, or to minimize computational effort. The MAD dataset we present here, despite containing fewer than 100k structures, has already been shown to enable training universal interatomic potentials that are competitive with models trained on traditional datasets with two to three orders of magnitude more structures.
Dataset authors
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux, Marnik Bercx, Giovanni Pizzi, Sandip De, Michele Ceriotti
Publication
https://doi.org/10.48550/arXiv.2506.19674
Original data link
https://doi.org/10.24435/materialscloud:vd-e8
License
CC-BY-4.0
Number of unique molecular configurations
76482
Number of atoms
2064229
Elements included
Ag, Al, Ar, As, Au, B, Ba, Be, Bi, Br, C, Ca, Cd, Ce, Cl, Co, Cr, Cs, Cu, Dy, Er, Eu, F, Fe, Ga, Gd, Ge, H, He, Hf, Hg, Ho, I, In, Ir, K, Kr, La, Li, Lu, Mg, Mn, Mo, N, Na, Nb, Nd, Ne, Ni, O, Os, P, Pb, Pd, Pm, Po, Pr, Pt, Rb, Re, Rh, Rn, Ru, S, Sb, Sc, Se, Si, Sm, Sn, Sr, Ta, Tb, Tc, Te, Ti, Tl, Tm, V, W, Xe, Y, Yb, Zn, Zr
Properties included
energy, atomic forces, cauchy stress
Usage
ds.parquet: Aggregated dataset information.co/directory: Configuration rows each include a structure, calculated properties, and metadata.cs/directory : Configuration sets are subsets of configurations grouped by some common characteristic. Ifcs/does not exist, no configurations sets have been defined for this dataset.cs_co_map/directory : The mapping of configurations to configuration sets (if defined).
ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
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