activity
20182021
most citedExceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles

625 citations · 1.3k across the 10 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

physics.chem-ph2019

Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for Gas-Phase Barrierless Reactions: Application to S + H2

Ivan S. Novikov, Alexander V. Shapeev, Yury V. Suleimanov

Ring polymer molecular dynamics (RPMD) has proven to be an accurate approach for calculating thermal rate coefficients of various chemical reactions. For wider application of this…

physics.comp-ph2019

Prediction of C7N6 and C9N4: Stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties

Bohayra Mortazavi, Masoud Shahrokhi, Alexander V Shapeev +2

In this work, three novel porous carbon-nitride nanosheets with C7N6, C9N4 and C10N3 stoichiometries are predicted. First-principles simulations were accordingly employed to evalua…

physics.comp-ph2019

A Performance and Cost Assessment of Machine Learning Interatomic Potentials

Yunxing Zuo, Chi Chen, Xiangguo Li +8

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the d…

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…

cond-mat.mtrl-sci2019

Ab initio vibrational free energies including anharmonicity for multicomponent alloys

Blazej Grabowski, Yuji Ikeda, Fritz Körmann +4

A density-functional-theory based approach to efficiently compute numerically exact vibrational free energies - including anharmonicity - for chemically complex multicomponent allo…

cs.LG2019

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev +1

Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation. Such an approach requires eithe…