Dataset
Massive_Atomic_Diversity_MAD_val
Species content of dataset
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Name :
Massive_Atomic_Diversity_MAD_val
Extended ID :
ColabFit ID :
Files :
Description :
The validation 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.
Authors :
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux, Marnik Bercx, Giovanni Pizzi, Sandip De, Michele Ceriotti
DOI :
10.60732/8ff541c9
https://commons.datacite.org/doi.org/10.60732/8ff541c9
https://doi.datacite.org/dois/10.60732%2F8ff541c9
https://doi.org/10.60732/8ff541c9
Cite as: Mazitov, A., Chorna, S., Fraux, G., Bercx, M., Pizzi, G., De, S., and Ceriotti, M. "Massive Atomic Diversity MAD val." ColabFit, 2025. https://doi.org/10.60732/8ff541c9.
For other citation formats, see the DataCite Fabrica page for this dataset.
For other citation formats, see the DataCite Fabrica page for this dataset.
Num. Configurations :
9,566
Num. Atoms :
257,052
Downloads :
14
Calculated Property Types :
atomic_forces
cauchy_stress
energy
Elements :
Ag (0.46%)
Al (0.83%)
Ar (0.01%)
As (0.67%)
Au (0.24%)
B (0.81%)
Ba (0.4%)
Be (0.26%)
Bi (0.47%)
Br (0.8%)
C (13.61%)
Ca (0.72%)
Cd (0.37%)
Ce (0.01%)
Cl (2.39%)
Co (0.65%)
Cr (0.37%)
Cs (0.7%)
Cu (0.82%)
Dy (0.02%)
Er (0.01%)
Eu (0.01%)
F (3.72%)
Fe (0.72%)
Ga (0.63%)
Gd (0.01%)
Ge (0.78%)
H (18.5%)
He (0.01%)
Hf (0.31%)
Hg (0.24%)
Ho (0.01%)
I (0.72%)
In (0.44%)
Ir (0.25%)
K (1.09%)
Kr (0.02%)
La (0.01%)
Li (0.87%)
Lu (0.01%)
Mg (0.45%)
Mn (0.53%)
Mo (0.59%)
N (5.91%)
Na (1.0%)
Nb (0.65%)
Nd (0.01%)
Ne (0.01%)
Ni (0.76%)
O (19.23%)
Os (0.11%)
P (1.71%)
Pb (0.48%)
Pd (0.53%)
Pm (0.01%)
Po (0.02%)
Pr (0.01%)
Pt (0.4%)
Rb (0.58%)
Re (0.2%)
Rh (0.28%)
Rn (0.01%)
Ru (0.23%)
S (3.24%)
Sb (0.66%)
Sc (0.37%)
Se (1.49%)
Si (1.22%)
Sm (0.01%)
Sn (0.56%)
Sr (0.7%)
Ta (0.38%)
Tb (0.01%)
Tc (0.07%)
Te (0.74%)
Ti (0.56%)
Tl (0.43%)
Tm (0.01%)
V (0.57%)
W (0.34%)
Xe (0.03%)
Y (0.62%)
Yb (0.01%)
Zn (0.6%)
Zr (0.7%)
Methods :
DFT-PBEsol
Software :
VASP
Publication Link :
Data Source Link :
Configuration Sets by Name :
Configuration Sets by ID :
Name: Massive_Atomic_Diversity_MAD_val
Extended ID: Massive_Atomic_Diversity_MAD_val__Mazitov-Chorna-Fraux-Bercx-Pizzi-De-Ceriotti__DS_xsog5nx0do2h_0
Description: The validation 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.
Authors:
Arslan Mazitov
Sofiia Chorna
Guillaume Fraux
Marnik Bercx
Giovanni Pizzi
Sandip De
Michele Ceriotti
DOI: 10.60732/8ff541c9
Calculated Property Types:
atomic_forces
cauchy_stress
energy
Elements:
Ag (0.46%)
Al (0.83%)
Ar (0.01%)
As (0.67%)
Au (0.24%)
B (0.81%)
Ba (0.4%)
Be (0.26%)
Bi (0.47%)
Br (0.8%)
C (13.61%)
Ca (0.72%)
Cd (0.37%)
Ce (0.01%)
Cl (2.39%)
Co (0.65%)
Cr (0.37%)
Cs (0.7%)
Cu (0.82%)
Dy (0.02%)
Er (0.01%)
Eu (0.01%)
F (3.72%)
Fe (0.72%)
Ga (0.63%)
Gd (0.01%)
Ge (0.78%)
H (18.5%)
He (0.01%)
Hf (0.31%)
Hg (0.24%)
Ho (0.01%)
I (0.72%)
In (0.44%)
Ir (0.25%)
K (1.09%)
Kr (0.02%)
La (0.01%)
Li (0.87%)
Lu (0.01%)
Mg (0.45%)
Mn (0.53%)
Mo (0.59%)
N (5.91%)
Na (1.0%)
Nb (0.65%)
Nd (0.01%)
Ne (0.01%)
Ni (0.76%)
O (19.23%)
Os (0.11%)
P (1.71%)
Pb (0.48%)
Pd (0.53%)
Pm (0.01%)
Po (0.02%)
Pr (0.01%)
Pt (0.4%)
Rb (0.58%)
Re (0.2%)
Rh (0.28%)
Rn (0.01%)
Ru (0.23%)
S (3.24%)
Sb (0.66%)
Sc (0.37%)
Se (1.49%)
Si (1.22%)
Sm (0.01%)
Sn (0.56%)
Sr (0.7%)
Ta (0.38%)
Tb (0.01%)
Tc (0.07%)
Te (0.74%)
Ti (0.56%)
Tl (0.43%)
Tm (0.01%)
V (0.57%)
W (0.34%)
Xe (0.03%)
Y (0.62%)
Yb (0.01%)
Zn (0.6%)
Zr (0.7%)
Methods:
DFT-PBEsol
Software:
VASP
Number of Configurations: 9,566
Number of Atoms: 257,052
Publication Link: https://doi.org/10.48550/arXiv.2506.19674
Data Source Link: https://doi.org/10.24435/materialscloud:vd-e8
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