4 papers
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…
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
A. A. Solovykh, N. E. Rybin, I. S. Novikov +1
Accounting for nuclear quantum effects (NQEs) can significantly alter material properties at finite temperatures. Atomic modeling using the path-integral molecular dynamics (PIMD)…