706 citations · 1.8k across the 13 of their papers we have counts for
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Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
Bohayra Mortazavi, Evgeny P. Podryabinkin, Ivan S. Nvikovb +3
Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured…
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…
Predicting the propensity for thermally activated events in metallic glasses via interpretable machine learning
Qi Wang, Jun Ding, Evan Ma
The elementary excitations in metallic glasses (MGs), i.e., processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys.…
High thermal conductivity in semiconducting Janus and non-Janus diamanes
Mostafa Raeisi, Bohayra Mortazavi, Evgeny V. Podryabinkin +3
Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and thermal conductivity…
Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
Bohayra Mortazavi, Ivan S. Novikov, Evgeny V. Podryabinkin +4
Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations o…
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…