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20182020
most citedAccelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

224 citations · 606 across the 7 of their papers we have counts for

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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…

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…

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…

cond-mat.mtrl-sci20201 cited

Ab initio analysis of some Ge-based 2D nanomaterials

Ali Ghojavand, S. Javad Hashemifar, Mahdi Tarighi Ahmadpour +3

The structural, electronic and dynamical properties of a group of 2D germanium-based compounds, including GeC, GeN, GeO, GeSi, GeS, GeSe, and germanene, are investigated by employi…

cond-mat.mtrl-sci20192 cited

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…