625 citations · 1.3k across the 10 of their papers we have counts for
8 papers · 1 filter
Moment tensor Potentials as a Promising Tool to Study Diffusion Processes
I. I. Novoselov, A. V. Yanilkin, A. V. Shapeev +1
A recently proposed class of machine-learning interatomic potentials --- Moment tensor potentials (MTPs) --- is investigated in this work. MTPs are able to actively select configur…
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…
Improving accuracy of interatomic potentials: more physics or more data? A case study of silica
Ivan S. Novikov, Alexander V. Shapeev
In this paper we test two strategies to improving the accuracy of machine-learning potentials, namely adding more fitting parameters thus making use of large volumes of available q…
Dropout-based Active Learning for Regression
Evgenii Tsymbalov, Maxim Panov, Alexander Shapeev
Active learning is relevant and challenging for high-dimensional regression models when the annotation of the samples is expensive. Yet most of the existing sampling methods cannot…
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…