colabfit/Massive_Atomic_Diversity_MAD-1.5_r2SCAN_Test

<details><summary>Cite this dataset </summary>Malosso, C., Bigi, F., Pegolo, P., Abbott, J. W., Loche, P., Rossi, M., Ceriotti, M., and Mazitov, A. Massive Atomic Diversity MAD-1.5 r2SCAN Test. ColabFit, 2026. https://doi.org/None</details>

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Dataset Name

Massive Atomic Diversity MAD-1.5 r2SCAN Test

Description

Test split of the MAD-1.5 (Massive Atomic Diversity version 1.5) dataset, a highly curated collection designed for training broadly applicable atomistic machine-learning models across the full periodic table. MAD-1.5 extends the original MAD dataset with targeted enrichment strategies covering 102 chemical elements (all isotopes with half-life above one day). All 216,803 structures are computed with a single standardized all-electron DFT workflow using the r2SCAN meta-GGA functional in FHI-aims (version 250806), with tight basis sets, 8 Angstrom^-1 k-point density, Gaussian smearing of 0.05 eV, and SCF convergence thresholds of 1e-6 eV (energy), 1e-4 eV/Angstrom (forces), and 1e-5 e*a0^-3 (electron density). The dataset spans molecules (monomers, dimers, trimers, molecular crystals), bulk crystals, surfaces, nanoclusters, and low-dimensional structures organized into 14 subsets. Quality is ensured by two-step outlier removal: heuristic filtering of structures with forces >100 eV/Angstrom, followed by LLPR uncertainty-based filtering. The test split (~10% of cleaned data, excluding monomers, dimers, and trimers which are fixed in the training split) uses a stratified split method consistent with the training and validation splits. Subset-resolved MAE for PET-MAD-1.5-S on this test set is 11.09 meV/atom (energy) and 36.81 meV/Angstrom (forces). A companion PBE-functional dataset (MassiveAtomicDiversityMAD-1.5PBE) was used during model training with separate prediction heads.

Dataset authors

Cesare Malosso, Filippo Bigi, Paolo Pegolo, Joseph W. Abbott, Philip Loche, Mariana Rossi, Michele Ceriotti, Arslan Mazitov

Publication

https://doi.org/10.48550/arXiv.2603.02089

Original data link

https://doi.org/10.24435/materialscloud:jc-9f

License

CC-BY-4.0

Number of unique molecular configurations

18314

Number of atoms

321704

Elements included

Ac, Ag, Al, Am, Ar, As, At, Au, B, Ba, Be, Bi, Bk, Br, C, Ca, Cd, Ce, Cf, Cl, Cm, Co, Cr, Cs, Cu, Dy, Er, Es, Eu, F, Fe, Fm, Fr, Ga, Gd, Ge, H, He, Hf, Hg, Ho, I, In, Ir, K, Kr, La, Li, Lu, Md, Mg, Mn, Mo, N, Na, Nb, Nd, Ne, Ni, No, Np, O, Os, P, Pa, Pb, Pd, Pm, Po, Pr, Pt, Pu, Ra, Rb, Re, Rh, Rn, Ru, S, Sb, Sc, Se, Si, Sm, Sn, Sr, Ta, Tb, Tc, Te, Th, Ti, Tl, Tm, U, V, W, Xe, Y, Yb, Zn, Zr

Properties included

energy, atomization energy, atomic forces, cauchy stress <br> <hr>

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. If cs/ 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). <br>

ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:

• —Parquet parsing: example code
• —Dataset info schema
• —Configuration schema
• —Configuration set schema
• —Configuration set to configuration mapping schema