activity
20162023
most citedActive learning of linearly parametrized interatomic potentials

706 citations · 1.8k across the 13 of their papers we have counts for

collaborators

14 papers

cond-mat.mtrl-sci2023

Mechanical properties of single and polycrystalline solids from machine learning

Faridun N. Jalolov, Evgeny V. Podryabinkin, Artem R. Oganov +2

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of methods and low accuracy of empirical…

cond-mat.mtrl-sci2023

A machine learning potential-based generative algorithm for on-lattice crystal structure prediction

Vadim Sotskov, Alexander V. Shapeev, Evgeny V. Podryabinkin

We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our algorithm generates t…

physics.comp-ph2023★ 3 cited

MLIP-3: Active learning on atomic environments with Moment Tensor Potentials

Evgeny Podryabinkin, Kamil Garifullin, Alexander Shapeev +1

Nowadays, academic research relies not only on sharing with the academic community the scientific results obtained by research groups while studying certain phenomena, but also on…

cond-mat.mtrl-sci2020★ 224 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.…