most citedAccelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

224 citations · 226 across the 2 of their papers we have counts for

collaborators

15 papers

cond-mat.mtrl-sci2020224 cited

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…

physics.comp-ph2020

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…

cond-mat.mtrl-sci2020

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.…

cond-mat.mtrl-sci202061 cited

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…

physics.comp-ph2020162 cited

Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties

Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi +4

Carbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous progress, most recentl…

cond-mat.mtrl-sci2020156 cited

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…