112 citations · 279 across the 14 of their papers we have counts for
14 papers
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…
Searching for ductile superconducting Heusler X2YZ compounds
Noah Hoffmann, Tiago F. T. Cerqueira, Pedro Borlido +3
Heusler compounds have always attracted a great deal of attention from researchers thanks to a wealth of interesting properties for technological applications. They are intermetall…
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.…
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,…
Machine-learning correction to density-functional crystal structure optimization
Robert Hussein, Jonathan Schmidt, Tomás Barros +2
Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE)…
Machine learning the derivative discontinuity of density-functional theory
Johannes Gedeon, Jonathan Schmidt, Matthew J. P. Hodgson +3
Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned func…
Machine Learning Universal Bosonic Functionals
Jonathan Schmidt, Matteo Fadel, Carlos L. Benavides-Riveros
The one-body reduced density matrix plays a fundamental role in describing and predicting quantum features of bosonic systems, such as Bose-Einstein condensation. The recently…
Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory
Jonathan Schmidt, Carlos L. Benavides-Riveros, Miguel A. L. Marques
We train a neural network as the universal exchange-correlation functional of density-functional theory that simultaneously reproduces both the exact exchange-correlation energy an…
Reduced Density Matrix Functional Theory for Superconductors
Jonathan Schmidt, Carlos L. Benavides-Riveros, Miguel A. L. Marques
We present an \textit{ab initio} theory for superconductors, based on a unique mapping between the statistical density operator at equilibrium, on the one hand, and the correspondi…
A representability problem in density functional theory for superconductors
Jonathan Schmidt, Carlos L. Benavides-Riveros, Miguel A. L. Marques
Aiming at a unified treatment of correlation and inhomogeneity effects in superconductors, Oliveira, Gross and Kohn proposed in 1988 a density functional theory for the superconduc…