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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…
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…
First-principles Hubbard parameters with automated and reproducible workflows
Lorenzo Bastonero, Cristiano Malica, Eric Macke +4
We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard and parameters from first-principles. By leveraging density-functional…
Common workflows for computing material properties using different quantum engines
Sebastiaan P. Huber, Emanuele Bosoni, Marnik Bercx +23
The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increa…
Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
Kamal Choudhary, Marnik Bercx, Jie Jiang +3
Solar-energy plays an important role in solving serious environmental problems and meeting high-energy demand. However, the lack of suitable materials hinders further progress of t…