224 citations · 654 across the 20 of their papers we have counts for
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AI-accelerated Materials Informatics Method for the Discovery of Ductile Alloys
Ivan Novikov, Olga Kovalyova, Alexander Shapeev +1
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that…
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…
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…