9 papers
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…
MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
Sebastiaan P. Huber, Michail Minotakis, Marnik Bercx +7
DFT is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, w…
Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app
Xing Wang, Edan Bainglass, Miki Bonacci +23
Despite the wide availability of density functional theory (DFT) codes, their adoption by the broader materials science community remains limited due to challenges such as software…
Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds
Marnik Bercx, Samuel Poncé, Yiming Zhang +8
We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud 3-dimensional structure database of experimentally kn…
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…