112 citations · 279 across the 14 of their papers we have counts for
8 papers · 1 filter
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
Universal Machine Learning Potentials under Pressure
Antoine Loew, Jonathan Schmidt, Silvana Botti +1
Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable per…
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…
Symmetry-based computational search for novel binary and ternary 2D materials
Hai-Chen Wang, Jonathan Schmidt, Miguel A. L. Marques +2
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs…
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.…