49 citations · 63 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, Johan Bergsma, Andrius Merkys +56
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enh…
Transfer learning on large datasets for the accurate prediction of material properties
Noah Hoffmann, Jonathan Schmidt, Silvana Botti +1
Graph neural networks trained on large crystal structure databases are extremely effective in replacing ab initio calculations in the discovery and characterization of materials. H…
Large-scale machine-learning-assisted exploration of the whole materials space
Jonathan Schmidt, Noah Hoffmann, Hai-Chen Wang +5
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures.…
Machine Learning guided high-throughput search of non-oxide garnets
Jonathan Schmidt, Haichen Wang, Georg Schmidt +1
Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries,…