2 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning
Timo Reents, Marnik Bercx, Giovanni Pizzi
Crystal structure databases curated by high-throughput density functional theory calculations typically serve as the starting point for computational materials discovery efforts. T…
Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions
Timo Reents, Arianna Cantarella, Marnik Bercx +2
Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with des…
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux +4
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the pa…