2 papers
cond-mat.mtrl-sci2026
AI-Driven Expansion and Application of the Alexandria Database
Théo Cavignac, Jonathan Schmidt, Pierre-Paul De Breuck +9
We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stabili…
cond-mat.supr-con2026
Ab-initio superfluid weight and superconducting penetration depth
Kaja H. Hiorth, Martin Gutierrez-Amigo, Théo Cavignac +3
Machine learning and high-throughput screening approaches to superconductor discovery require physically meaningful descriptors that capture essential physics while remaining compu…