The MLIP package: moment tensor potentials with MPI and active learning IS Novikov, K Gubaev, EV Podryabinkin, AV Shapeev Machine Learning: Science and Technology 2 (2), 025002, 2020 | 534 | 2020 |
Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials B Mortazavi, IS Novikov, EV Podryabinkin, S Roche, T Rabczuk, ... Applied Materials Today 20, 100685, 2020 | 193 | 2020 |
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution B Mortazavi, EV Podryabinkin, IS Novikov, T Rabczuk, X Zhuang, ... Computer Physics Communications 258, 107583, 2021 | 167 | 2021 |
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials B Mortazavi, EV Podryabinkin, IS Novikov, S Roche, T Rabczuk, ... Journal of Physics: Materials 3 (2), 02LT02, 2020 | 85 | 2020 |
Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe I Novikov, B Grabowski, F Körmann, A Shapeev npj Computational Materials 8 (1), 13, 2022 | 82 | 2022 |
A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers B Mortazavi, IS Novikov, AV Shapeev Carbon 188, 431-441, 2022 | 55 | 2022 |
Improving accuracy of interatomic potentials: more physics or more data? A case study of silica IS Novikov, AV Shapeev Materials Today Communications 18, 74-80, 2019 | 53 | 2019 |
Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning IS Novikov, YV Suleimanov, AV Shapeev Physical Chemistry Chemical Physics 20 (46), 29503-29512, 2018 | 51 | 2018 |
MLIP-3: Active learning on atomic environments with Moment Tensor Potentials E Podryabinkin, K Garifullin, A Shapeev, I Novikov The Journal of Chemical Physics 159, 084112, 2023 | 31 | 2023 |
Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S+ H2 IS Novikov, AV Shapeev, YV Suleimanov The Journal of chemical physics 151 (22), 2019 | 20 | 2019 |
Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al AS Kotykhov, K Gubaev, M Hodapp, C Tantardini, AV Shapeev, ... Scientific Reports 13 (1), 19728, 2023 | 10 | 2023 |
Machine-learning interatomic potentials reproduce vibrational and magnetic degrees of freedom I Novikov, B Grabowski, F Kormann, A Shapeev Preprint at https://arxiv. org/abs/2012.12763, 2020 | 10 | 2020 |
AI-accelerated materials informatics method for the discovery of ductile alloys I Novikov, O Kovalyova, A Shapeev, M Hodapp Journal of Materials Research 37 (21), 3491-3504, 2022 | 9 | 2022 |
Interatomic Interaction Models for Magnetic Materials: Recent Advances TS Kostiuchenko, AV Shapeev, IS Novikov Chinese Physics Letters 41 (6), 2024 | 6 | 2024 |
Problem of minimization of pollution concentration related to fires in Moscow region IS Novikov Russian Journal of Numerical Analysis and Mathematical Modelling 28 (1), 13-36, 2013 | 5 | 2013 |
Solution of the pollutant concentration optimization problem with restrictions on the intensity of sources VI Agoshkov, IS Novikov Computational Mathematics and Mathematical Physics 56, 26-42, 2016 | 3 | 2016 |
Accelerating structure prediction of molecular crystals using actively trained moment tensor potential N Rybin, IS Novikov, A Shapeev Physical Chemistry Chemical Physics, 2025 | 2 | 2025 |
Quantum modelling of magnetism in strongly correlated materials: Evaluating constrained DFT and the Hubbard model for Y114 C Tantardini, D Fazylbekova, SV Levchenko, IS Novikov Computational Materials Science 246, 113465, 2025 | 1 | 2025 |
Towards reliable calculations of thermal rate constants: Ring polymer molecular dynamics for the OH+ HBr→ Br+ H2O reaction IS Novikov, EM Makarov, YV Suleimanov, AV Shapeev Chemical Physics Letters, 141620, 2024 | 1 | 2024 |
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials AS Kotykhov, K Gubaev, V Sotskov, C Tantardini, M Hodapp, AV Shapeev, ... Computational Materials Science 245, 113331, 2024 | 1 | 2024 |