4 papers
Predicting Spin-Crossover Behavior in Metal-Organic Frameworks from Limited and Noisy Data Using Quantile Active Learning
Ashna Jose, Emilie Devijver, Martin Uhrin +2
Spin-crossover (SCO) metal-organic frameworks (MOFs) hold great promise for sensing, spintronics, and gas-related applications, however, only a small number of SCO-active examples…
Reproducible container solutions for codes and workflows in materials science
Dylan Bissuel, Léo Orveillon, Benjamin Arrondeau +15
A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible s…
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…
Machine learning Hubbard parameters with equivariant neural networks
Martin Uhrin, Austin Zadoks, Luca Binci +2
Density-functional theory with extended Hubbard functionals (DFT++) provides a robust framework to accurately describe complex materials containing transition-metal or rare-e…