Shapeev AV - Search Results

1

Constrained Density Functional Theory: A Potential-Based Self-Consistency Approach.

Gonze X, Seddon B, Elliott JA, Tantardini C, Shapeev AV. Gonze X, et al. Among authors: shapeev av. J Chem Theory Comput. 2022 Oct 11;18(10):6099-6110. doi: 10.1021/acs.jctc.2c00673. Epub 2022 Sep 13. J Chem Theory Comput. 2022. PMID: 36099643 Free PMC article.

2

Constrained DFT-based magnetic machine-learning potentials for magnetic alloys: a case study of Fe-Al.

Kotykhov AS, Gubaev K, Hodapp M, Tantardini C, Shapeev AV, Novikov IS. Kotykhov AS, et al. Among authors: shapeev av. Sci Rep. 2023 Nov 13;13(1):19728. doi: 10.1038/s41598-023-46951-x. Sci Rep. 2023. PMID: 37957211 Free PMC article.

3

Machine learning of molecular properties: Locality and active learning.

Gubaev K, Podryabinkin EV, Shapeev AV. Gubaev K, et al. Among authors: shapeev av. J Chem Phys. 2018 Jun 28;148(24):241727. doi: 10.1063/1.5005095. J Chem Phys. 2018. PMID: 29960350

4

Nanohardness from First Principles with Active Learning on Atomic Environments.

Podryabinkin EV, Kvashnin AG, Asgarpour M, Maslenikov II, Ovsyannikov DA, Sorokin PB, Popov MY, Shapeev AV. Podryabinkin EV, et al. Among authors: shapeev av. J Chem Theory Comput. 2022 Feb 8;18(2):1109-1121. doi: 10.1021/acs.jctc.1c00783. Epub 2022 Jan 6. J Chem Theory Comput. 2022. PMID: 34990122

5

Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H₂.

Novikov IS, Shapeev AV, Suleimanov YV. Novikov IS, et al. Among authors: shapeev av. J Chem Phys. 2019 Dec 14;151(22):224105. doi: 10.1063/1.5127561. J Chem Phys. 2019. PMID: 31837691

6

MLIP-3: Active learning on atomic environments with moment tensor potentials.

Podryabinkin E, Garifullin K, Shapeev A, Novikov I. Podryabinkin E, et al. J Chem Phys. 2023 Aug 28;159(8):084112. doi: 10.1063/5.0155887. J Chem Phys. 2023. PMID: 37638620

7

Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning.

Novikov IS , Suleimanov YV , Shapeev AV . Novikov IS , et al. Among authors: shapeev av. Phys Chem Chem Phys. 2018 Nov 28;20(46):29503-29512. doi: 10.1039/c8cp06037a. Phys Chem Chem Phys. 2018. PMID: 30457606

8

Young's Modulus and Tensile Strength of Ti₃C₂ MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations.

Firestein KL, von Treifeldt JE, Kvashnin DG, Fernando JFS, Zhang C, Kvashnin AG, Podryabinkin EV, Shapeev AV, Siriwardena DP, Sorokin PB, Golberg D. Firestein KL, et al. Among authors: shapeev av. Nano Lett. 2020 Aug 12;20(8):5900-5908. doi: 10.1021/acs.nanolett.0c01861. Epub 2020 Jul 13. Nano Lett. 2020. PMID: 32633975

9

First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials.

Mortazavi B, Silani M, Podryabinkin EV, Rabczuk T, Zhuang X, Shapeev AV. Mortazavi B, et al. Among authors: shapeev av. Adv Mater. 2021 Sep;33(35):e2102807. doi: 10.1002/adma.202102807. Epub 2021 Jul 23. Adv Mater. 2021. PMID: 34296779

10

Deep elastic strain engineering of bandgap through machine learning.

Shi Z, Tsymbalov E, Dao M, Suresh S, Shapeev A, Li J. Shi Z, et al. Proc Natl Acad Sci U S A. 2019 Mar 5;116(10):4117-4122. doi: 10.1073/pnas.1818555116. Epub 2019 Feb 15. Proc Natl Acad Sci U S A. 2019. PMID: 30770444 Free PMC article.

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