MAD is a compact dataset of distorted, randomized, and diverse atomic structures computed with consistent DFT settings, designed to train universal interatomic potentials with far fewer samples.
All-Electron APW+${lo}$ calculation of magnetic molecules with the SIRIUS domain-specific package
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abstract
We report APW+${lo}$ (augmented plane wave plus local orbital) density functional theory (DFT) calculations of molecule systems using the domain specific SIRIUS multi-functional DFT package. Compared to other packages the additional APW and FLAPW task and data parallelism and the additional eigensystem solver provided by the SIRIUS package can be exploited for performance gains in in the ground state Kohn-Sham calculation. This is in contrast with the use of SIRIUS as a library backend to some other APW+${lo}$ or FLAPW (full-potential linearized AWP) code. We benchmark the code and demonstrate performance on several magnetic molecule and metal organic framework systems. We show that the SIRIUS package in itself is capable of handling systems as large as a few hundreds of atoms in the unit cell without losing the accuracy needed for magnetic systems.
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Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
MAD is a compact dataset of distorted, randomized, and diverse atomic structures computed with consistent DFT settings, designed to train universal interatomic potentials with far fewer samples.