3 citations · 3 across the 4 of their papers we have counts for
5 papers
Compressed magnetic Moment Tensor Potentials via low-rank matrix and tensor factorizations
Alexey S. Kotykhov, Alexander V. Shapeev, Maxim Rakhuba +1
We propose a parameter-reduced version of magnetic Moment Tensor Potential (mMTP) based on various matrix and tensor decompositions. The resulting compressed magnetic machine-learn…
Low-rank approximation of Moment Tensor Potential enables reducing training set size without loss of accuracy
Anna Bondarenko, Nikita Rybin, Maxim Rakhuba +1
In this study, we implement a low-rank approximation of Moment Tensor Potential (MTP) based on the tensor train (TT) decomposition. The implemented tensor-factorized MTP (TFMTP) an…
Active learning of collinear magnetic Moment Tensor Potentials using the spin-MLIP package from soft-constrained spin-polarized DFT calculations: a case study of Fe-Pd
Arseniy Burov, Alexey S. Kotykhov, Dmitry A. Aksyonov +2
Explicit incorporation of magnetic degrees of freedom in machine-learning interatomic potentials (magnetic MLIPs) plays a crucial role in the correct description of magnetic materi…
Global Optimization of Atomic Clusters via Physically-Constrained Tensor Train Decomposition
Konstantin Sozykin, Nikita Rybin, Andrei Chertkov +5
The global optimization of atomic clusters represents a fundamental challenge in computational chemistry and materials science due to the exponential growth of local minima with sy…
Low-rank matrix and tensor approximations for compression of machine-learning interatomic potentials
Igor Vorotnikov, Fedor Romashov, Nikita Rybin +2
Machine-learning interatomic potentials (MLIPs) have become a mainstay in computationally-guided materials science, surpassing traditional force fields due to their flexible functi…