Ivan S. Novikov

Skolkovo Institute of Science and Technology — Russia

Materials Science · Physical Sciences

9h-index555citations25works2.62yr avg
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Research Topics

Machine Learning in Materials Science(14), Quantum, superfluid, helium dynamics(5), X-ray Diffraction in Crystallography(4), Advanced Chemical Physics Studies(3), High-pressure geophysics and materials(3)

Publications25 total

Incorporating Coulomb interactions with fixed charges in Moment Tensor Potentials and Equivariant Tensor Network Potentials
arXiv (Cornell University)·2025Open Access
Path-integral molecular dynamics with actively-trained and universal machine learning force fields
arXiv (Cornell University)·2025Open Access
Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions
arXiv (Cornell University)·2025· 1 citedOpen Access
Accelerating structure prediction of molecular crystals using actively trained moment tensor potential
Physical Chemistry Chemical Physics·2025· 6 cited
Actively-trained magnetic Moment Tensor Potentials for mechanical, dynamical, and thermal properties of paramagnetic CrN
arXiv (Cornell University)·2024Open Access
Accelerating Structure Prediction of Molecular Crystals using Actively Trained Moment Tensor Potential
arXiv (Cornell University)·2024Open Access
Fitting to magnetic forces improves the reliability of magnetic Moment Tensor Potentials
Computational Materials Science·2024· 7 citedOpen Access
Interatomic Interaction Models for Magnetic Materials: Recent Advances
arXiv (Cornell University)·2024· 1 citedOpen Access
Interatomic Interaction Models for Magnetic Materials: Recent Advances
Chinese Physics Letters·2024· 11 citedOpen Access
Quantum Modelling of Magnetism in Strongly Correlated Materials: Evaluating Constrained DFT and the Hubbard Model for Y114
arXiv (Cornell University)·2024Open Access
Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe–Al
Scientific Reports·2023· 23 citedOpen Access
AI-accelerated materials informatics method for the discovery of ductile alloys
Journal of materials research/Pratt's guide to venture capital sources·2022· 8 citedOpen Access
A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers
Carbon·2021· 65 cited
Assessing parameters for ring polymer molecular dynamics simulations at low temperatures: DH + H chemical reaction
Chemical Physics Letters·2021Open Access
Machine-learning interatomic potentials reproduce vibrational and magnetic degrees of freedom
arXiv (Cornell University)·2020· 4 citedOpen Access
The MLIP package: moment tensor potentials with MPI and active learning
Machine Learning Science and Technology·2020· 40 citedOpen Access
Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
Computer Physics Communications·2020· 203 citedOpen Access
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
Journal of Physics Materials·2020· 76 citedOpen Access
Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H2
The Journal of Chemical Physics·2019· 17 citedOpen Access
Experimentally Modeling Stochastic Resonance in the Duffing Oscillator
Murray State's Digital Commons (Murray State University)·2019
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Frequent Co-authors

Alexander V. Shapeev(22), Max Hodapp(6), Yury V. Suleimanov(5), Nikita Rybin(5), Christian Tantardini(4), Bohayra Mortazavi(3), Evgeny V. Podryabinkin(3), Konstantin Gubaev(3), Alexey S. Kotykhov(3), Timon Rabczuk(2), Xiaoying Zhuang(2), Tatiana Kostiuchenko(2), Olga Chalykh(2), Dmitry Korogod(2), Stephan Roche(1), Alexander V. Shapeev(1), O. Kovalyova(1), Vadim Sotskov(1), Blazej Grabowski(1), Fritz Körmann(1)