625 citations · 1.4k across the 14 of their papers we have counts for
4 papers · 2 filters
Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
Tatiana Kostiuchenko, Fritz Körmann, Jörg Neugebauer +1
Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficien…
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart +1
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for th…
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Evgeny V. Podryabinkin, Evgeny V. Tikhonov, Alexander V. Shapeev +1
In this letter we propose a new methodology for crystal structure prediction, which is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials act…