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Eliodoro Chiavazzo ⁴ and Nicola Marzari ^1,6,7

¹PSI Center for Scientific Computing, Theory, and Data, and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), 5232 Villigen PSI, Switzerland
²European Theoretical Spectroscopy Facility, Institute of Condensed Matter and Nanosciences, Université catholique de Louvain, Chemin des Étoiles 8, B-1348 Louvain-la-Neuve, Belgium
³WEL Research Institute, Avenue Pasteur 6, 1300 Wavre, Belgium
⁴Department of Energy, Politecnico di Torino, 10129 Torino TO, Italy
⁵Electronic Components, Technology and Materials (ECTM), TU Delft, 2628 CD Delft, Netherlands
⁶Bremen Center for Computational Materials Science, and MAPEX Center for Materials and Processes, University of Bremen, 28359 Bremen, Germany
⁷Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
⁸Department of Quantum Matter Physics, University of Geneva, 24 Quai Ernest-Ansermet, 1211 Geneva, Switzerland

^*Contact author: marnik.bercx@psi.ch
^†Contact author: samuel.ponce@uclouvain.be

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Articles Citing

Charting the Landscape of Bardeen-Cooper-Schrieffer Superconductors in Experimentally Known Compounds

Permutation-invariant deep learning for predicting superconducting critical temperatures from chemical composition

From symmetry to stability: Structural and electronic transformation in Cs2KInI6

Electron-phonon coupling in magnetic materials using the local spin density approximation

Ab initio superfluid weight and superconducting penetration depth

Describing materials via interpretable machine learning: the example of superconductivity

Carrier mobilities and electron-phonon interactions beyond DFT

Developing a complete AI-accelerated workflow for superconductor discovery

Machine learning driven exploration of hydride superconductors at ambient pressure

Low-melting-point metal routes toward making superconductors

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement

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