164 citations · 215 across the 5 of their papers we have counts for
6 papers
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
Alexey S. Kotykhov, Konstantin Gubaev, Vadim Sotskov +4
We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of ou…
The MLIP package: Moment Tensor Potentials with MPI and Active Learning
Ivan S. Novikov, Konstantin Gubaev, Evgeny V. Podryabinkin +1
The subject of this paper is the technology (the "how") of constructing machine-learning interatomic potentials, rather than science (the "what" and "why") of atomistic simulations…
Elinvar effect in Ti simulated by on-the-fly trained moment tensor potential
Alexander V. Shapeev, Evgeny V. Podryabinkin, Konstantin Gubaev +2
A combination of quantum mechanics calculations with machine learning (ML) techniques can lead to a paradigm shift in our ability to predict materials properties from first princip…
Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev +4
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tens…
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…
Machine learning of molecular properties: locality and active learning
Konstantin Gubaev, Evgeny V. Podryabinkin, Alexander V. Shapeev
In recent years the machine learning techniques have shown a great potential in various problems from a multitude of disciplines, including materials design and drug discovery. The…