List of Publications

Peer-Reviewed Publications:
2022
  • Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra (Alexander V Shapeev, Dmitry Bocharov, Alexei Kuzmin), Computational Materials Science, Elsevier, volume 210, pages 111028, 2022. [bibtex]
  • Nanohardness from First Principles with Active Learning on Atomic Environments (Evgeny V Podryabinkin, Alexander G Kvashnin, Milad Asgarpour, Igor I Maslenikov, Danila A Ovsyannikov, Pavel B Sorokin, Mikhail Yu Popov, Alexander V Shapeev), Journal of Chemical Theory and Computation, ACS Publications, volume 18, pages 1109–1121, 2022. [bibtex]
  • Magnetic Moment Tensor Potentials for collinear spin-polarized materials reproduce different magnetic states of bcc Fe (Ivan Novikov, Blazej Grabowski, Fritz Körmann, Alexander Shapeev), npj Computational Materials, Nature Publishing Group UK London, volume 8, pages 13, 2022. [bibtex]
  • AI-accelerated materials informatics method for the discovery of ductile alloys (Ivan Novikov, Olga Kovalyova, Alexander Shapeev, Max Hodapp), Journal of Materials Research, Springer, volume 37, pages 3491–3504, 2022. [bibtex]
  • Outstanding thermal conductivity and mechanical properties in the direct gap semiconducting penta-NiN2 monolayer confirmed by first-principles (Bohayra Mortazavi, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Physica E: Low-dimensional Systems and Nanostructures, Elsevier, volume 140, pages 115221, 2022. [bibtex]
  • Mechanical, thermal transport, electronic and photocatalytic properties of penta-PdPS,-PdPSe and-PdPTe monolayers explored by first-principles calculations (Bohayra Mortazavi, Masoud Shahrokhi, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Journal of Materials Chemistry C, Royal Society of Chemistry, volume 10, pages 329–336, 2022. [bibtex]
  • A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers (Bohayra Mortazavi, Ivan S Novikov, Alexander V Shapeev), Carbon, Elsevier, volume 188, pages 431–441, 2022. [bibtex]
  • A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C 5 N monolayers (Bohayra Mortazavi, Masoud Shahrokhi, Fazel Shojaei, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Nanoscale, Royal Society of Chemistry, volume 14, pages 4324–4333, 2022. [bibtex]
  • Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials (Bohayra Mortazavi, Ali Rajabpour, Xiaoying Zhuang, Timon Rabczuk, Alexander V Shapeev), Carbon, Elsevier, volume 186, pages 501–508, 2022. [bibtex]
  • Electronic, Optical, Mechanical and Li-Ion Storage Properties of Novel Benzotrithiophene-Based Graphdiyne Monolayers Explored by First Principles and Machine Learning (Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Batteries, MDPI, volume 8, pages 194, 2022. [bibtex]
  • A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides (Bohayra Mortazavi, Fazel Shojaei, Alexander V Shapeev, Xiaoying Zhuang), Carbon, Elsevier, volume 194, pages 230–239, 2022. [bibtex]
  • Anisotropic mechanical response, high negative thermal expansion, and outstanding dynamical stability of biphenylene monolayer revealed by machine-learning interatomic potentials (Bohayra Mortazavi, Alexander V Shapeev), FlatChem, Elsevier, volume 32, pages 100347, 2022. [bibtex]
  • Mechanical, optical, and thermoelectric properties of semiconducting ZnIn2X4 (X= S, Se, Te) monolayers (Mohammad Ali Mohebpour, Bohayra Mortazavi, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev, Meysam Bagheri Tagani), Physical Review B, APS, volume 105, pages 134108, 2022. [bibtex]
  • Constrained Density Functional Theory: a potential-based self-consistency approach (Xavier Gonze, Benjamin Seddon, James A Elliott, Christian Tantardini, Alexander V Shapeev), Journal of Chemical Theory and Computation, ACS Publications, volume 18, pages 6099–6110, 2022. [bibtex]
  • Short-range order and phase stability of CrCoNi explored with machine learning potentials (Sheuly Ghosh, Vadim Sotskov, Alexander V Shapeev, Jörg Neugebauer, Fritz Körmann), Physical Review Materials, APS, volume 6, pages 113804, 2022. [bibtex]
2021
  • Machine-learning potentials enable predictive and tractable high-throughput screening of random alloys (Max Hodapp, Alexander Shapeev), Physical Review Materials, APS, volume 5, pages 113802, 2021. [bibtex]
  • Modeling the high-temperature phase coexistence region of mixed transition metal oxides from ab initio calculations (Suzanne K Wallace, Ambroise van Roekeghem, Anton S Bochkarev, Javier Carrasco, Alexander Shapeev, Natalio Mingo), Physical Review Research, APS, volume 3, pages 013139, 2021. [bibtex]
  • Free energy of (Co x Mn 1- x)3 O4 mixed phases from machine-learning-enhanced ab initio calculations (Suzanne K Wallace, Anton S Bochkarev, Ambroise van Roekeghem, Javier Carrasco, Alexander Shapeev, Natalio Mingo), Physical Review Materials, APS, volume 5, pages 035402, 2021. [bibtex]
  • Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass (Evgenii Tsymbalov, Zhe Shi, Ming Dao, Subra Suresh, Ju Li, Alexander Shapeev), npj Computational Materials, Nature Publishing Group, volume 7, pages 1–10, 2021. [bibtex]
  • Efficient and accurate prediction of elastic properties of Ti0. 5Al0. 5N at elevated temperature using machine learning interatomic potential (Ferenc Tasnádi, Florian Bock, Johan Tidholm, Alexander V Shapeev, Igor A Abrikosov), Thin Solid Films, Elsevier, pages 138927, 2021. [bibtex]
  • Machine-learned interatomic potentials for alloys and alloy phase diagrams (Conrad W Rosenbrock, Konstantin Gubaev, Alexander V Shapeev, Livia B Pártay, Noam Bernstein, Gábor Csányi, Gus LW Hart), npj Computational Materials, Nature Publishing Group, volume 7, pages 1–9, 2021. [bibtex]
  • Assessing parameters for ring polymer molecular dynamics simulations at low temperatures: DH+ H chemical reaction (Ivan S Novikov, Yury V Suleimanov, Alexander V Shapeev), Chemical Physics Letters, Elsevier, volume 773, pages 138567, 2021. [bibtex]
  • Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles (Bohayra Mortazavi, Brahmanandam Javvaji, Fazel Shojaei, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Nano Energy, Elsevier, volume 82, pages 105716, 2021. [bibtex]
  • Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution (Bohayra Mortazavi, Evgeny V Podryabinkin, Ivan S Novikov, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Computer Physics Communications, Elsevier, volume 258, pages 107583, 2021. [bibtex]
  • First-Principles Multiscale Modeling of Mechanical Properties in Graphene/Borophene Heterostructures Empowered by Machine-Learning Interatomic Potentials (Bohayra Mortazavi, Mohammad Silani, Evgeny V Podryabinkin, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Advanced Materials, Wiley Online Library, pages 2102807, 2021. [bibtex]
  • Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams (V. Ladygin, I. Beniya, E. Makarov, A. Shapeev), Physical Review B, APS, volume 104, pages 104102, 2021. [bibtex] [doi]
  • B2 ordering in body-centered-cubic AlNbTiV refractory high-entropy alloys (Fritz Körmann, Tatiana Kostiuchenko, Alexander Shapeev, Jörg Neugebauer), Physical Review Materials, APS, volume 5, pages 053803, 2021. [bibtex]
  • Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials (Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnádi, Jörg Neugebauer, Alexander V Shapeev, Blazej Grabowski, Fritz Körmann), Physical Review Materials, APS, volume 5, pages 073801, 2021. [bibtex]
  • Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials (Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnádi, Jörg Neugebauer, Alexander V Shapeev, Blazej Grabowski, Fritz Körmann), Physical Review Materials, APS, volume 5, pages 073801, 2021. [bibtex]
  • Ab initio simulations of the surface free energy of TiN (001) (Axel Forslund, Xi Zhang, Blazej Grabowski, Alexander V Shapeev, Andrei V Ruban), Physical Review B, APS, volume 103, pages 195428, 2021. [bibtex]

2020

  • Performance and Cost Assessment of Machine Learning Interatomic Potentials (Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V Shapeev, Aidan P Thompson, Mitchell A Wood, others), The Journal of Physical Chemistry A, ACS Publications, volume 124, pages 731–745, 2020. [bibtex][arXiv]
  • Metallization of diamond (Zhe Shi, Ming Dao, Evgenii Tsymbalov, Alexander Shapeev, Ju Li, Subra Suresh), Proceedings of the National Academy of Sciences, National Acad Sciences, volume 117, pages 24634–24639, 2020. [bibtex]
  • High thermal conductivity in semiconducting Janus and non-Janus diamanes (Mostafa Raeisi, Bohayra Mortazavi, Evgeny V Podryabinkin, Fazel Shojaei, Xiaoying Zhuang, Alexander V Shapeev), Carbon, Elsevier, 2020. [bibtex]
  • The MLIP package: Moment Tensor Potentials with MPI and Active Learning (Ivan S Novikov, Konstantin Gubaev, Evgeny Podryabinkin, Alexander V Shapeev), Machine Learning: Science and Technology, Elsevier, 2020. [bibtex][doi]
  • Nanoporous C3N4, C3N5 and C3N6 nanosheets; novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties (Bohayra Mortazavi, Fazel Shojaei, Masoud Shahrokhi, Maryam Azizi, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Carbon, IOP Publishing, 2020. [bibtex]
  • Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures (Bohayra Mortazavi, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Materials Horizons, Royal Society of Chemistry, volume 7, pages 2359–2367, 2020. [bibtex]
  • Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials (Bohayra Mortazavi, Ivan S Novikov, Evgeny V Podryabinkin, Stephan Roche, Timon Rabczuk, Alexander V Shapeev, Xiaoying Zhuang), Applied Materials Today, Elsevier, volume 20, pages 100685, 2020. [bibtex][pdf][doi]
  • Efficient machine-learning based interatomic potentials for exploring thermal conductivity in two-dimensional materials (Bohayra Mortazavi, Evgeny Podryabinkin, Ivan S Novikov, Stephan Roche, Timon Rabczuk, Xiaoying Zhuang, Alexander Shapeev), Journal of Physics: Materials, IOP Publishing, 2020.[bibtex][doi]
  • Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution (Bohayra Mortazavi, Evgeny V Podryabinkin, Ivan S Novikov, Timon Rabczuk, Xiaoying Zhuang, Alexander V Shapeev), Computer Physics Communications, Elsevier, pages 107583, 2020. [bibtex][pdf][doi]
  • Lattice dynamics simulation using machine learning interatomic potentials (VV Ladygin, P Yu Korotaev, AV Yanilkin, AV Shapeev), Computational Materials Science, Elsevier, volume 172, pages 109333, 2020. [bibtex][pdf][doi]
  • Short-range order in face-centered cubic VCoNi alloys (Tatiana Kostiuchenko, Andrei V Ruban, Jörg Neugebauer, Alexander Shapeev, Fritz Körmann), Physical Review Materials, APS, volume 4, pages 113802, 2020. [bibtex]
  • Ab initio analysis of structural and electronic properties and excitonic optical responses of eight Ge-based 2D materials (Ali Ghojavand, S Javad Hashemifar, Mahdi Tarighi Ahmadpour, Alexander V Shapeev, Amir Alhaji, Qaem Hassanzada), Journal of Applied Physics, AIP Publishing LLC, volume 127, pages 214301, 2020. [bibtex]
  • Young’s Modulus and Tensile Strength of Ti3C2 MXene Nanosheets As Revealed by In Situ TEM Probing, AFM Nanomechanical Mapping, and Theoretical Calculations (Konstantin L Firestein, Joel E von Treifeldt, Dmitry G Kvashnin, Joseph FS Fernando, Chao Zhang, Alexander G Kvashnin, Evgeny V Podryabinkin, Alexander V Shapeev, Dumindu P Siriwardena, Pavel B Sorokin, others), Nano Letters, ACS Publications, volume 20, pages 5900–5908, 2020. [bibtex]

2019

  • Ring polymer molecular dynamics and active learning of moment tensor potential for gas-phase barrierless reactions: Application to S + H2 (I.S. Novikov, A.V. Shapeev, Y.V. Suleimanov), Journal of Chemical Physics, v. 151, p. 224105, 2019. [doi]
  • Accessing thermal conductivity of complex compounds by machine learning interatomic potentials (P. Korotaev, I. Novoselov, A. Yanilkin, A. Shapeev), Physical Review B, APS, v. 100, pages 144308, 2019. [doi]
  • Ab initio vibrational free energies including anharmonicity for multicomponent alloys (B. Grabowski, Y. Ikeda, P. Srinivasan, F. Körmann, C. Freysoldt, A.I. Duff, A. Shapeev, J. Neugebauer), npj Computational Materials, Nature Publishing Group, v. 5, pages 1-6, 2019. [doi]
  • Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning (E. Tsymbalov, S. Makarychev, Shapeev A., Panov M.), Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Main track. Pages 3599-3605, 2019. [pdf] [doi]
  • Sublattice formation in CoCrFeNi high-entropy alloy (E.A. Meshkov, I.I. Novoselov, A.V. Shapeev, A.V. Yanilkin), Intermetallics, Elsevier, volume 112, pages 106542, 2019. [bibtex] [pdf] [doi]
  • Applying a machine learning interatomic potential to unravel the effects of local lattice distortion on the elastic properties of multi-principal element alloys (M. Jafary-Zadeh, K.H. Khoo, R. Laskowski, P.S. Branicio, A. Shapeev), Journal of Alloys and Compounds, Elsevier, 2019. [bibtex] [pdf] [doi]
  • Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials (T. Kostiuchenko, F. Körmann, J. Neugebauer, A. Shapeev), npj Computational Materials, Nature Publishing Group, v. 5, No. 55, 2019. [pdf] [doi] [arXiv]
  • Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning (E. Podryabinkin, E. Tikhonov, A. Shapeev, A. Oganov), Physical Review B, APS, volume 99, pages 064114, 2019. [bibtex] [pdf] [doi] [arXiv]
  • Deep elastic strain engineering of bandgap through machine learning (Z. Shi, E. Tsymbalov, M. Dao, S. Suresh, A. Shapeev, J. Li),  Proceedings of the National Academy of Sciences , v. 116, p. 4117-4122, 2019.[bibtex] [pdf] [doi]
  • Machine-learned multi-system surrogate models for materials prediction (G. Nyshadham, M. Rupp, B. Bekker, A. Shapeev, T. Mueller, C. Rosenbrock, G. Csányi, D. Wingate, G. Hart), npj Computational Materials, Nature Publishing Group, v. 5, p. 51, 2019. [bibtex] [pdf] [doi] [arXiv]
  • Accelerating high-throughput searches for new alloys with active learning of interatomic potentials (K. Gubaev, E. V. Podryabinkin, G. L. W. Hart, A. V. Shapeev), Computational Materials Science, v. 156, p. 148-156, 2019. [doi] [arXiv]
  • Improving accuracy of interatomic potentials: more physics or more data? A case study of silica (I.Novikov, A.Shapeev), Materials Today Communications, v. 18, p. 74-80, 2019. [doi] [arXiv]
  • Moment tensor potentials as a promising tool to study diffusion processes (I.I. Novoselov, A.V. Yanilkin, A.V. Shapeev, E.V. Podryabinkin), Computational Materials Science, v. 165, p. 46–56, 2019. [bibtex] [pdf] [doi] [arXiv]

 

2018
  • Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning (I. Novikov, Y. Suleimanov, A. Shapeev), Physical Chemistry Chemical Physics, v. 20, p. 29503-29512, 2018. [doi] [arXiv]
  • Machine Learning of Molecular Properties: Locality and Active Learning (K. Gubaev, E .V. Podryabinkin, A. V. Shapeev), The Journal of Chemical Physics, volume 148, 241727, 2018. [doi] [arXiv]

 

2017
  • Active Learning of Linearly Parametrized Interatomic Potentials (E. V. Podryabinkin, A. V. Shapeev), Computational Materials Science, volume 140, pages 171-180, 2017. [bibtex] [doi] [arXiv]
  • Accurate representation of formation energies of crystalline alloys with many components (A. Shapeev),  Computational Materials Science, volume 139, pages 26-30, 2017. [bibtex] [doi] [arXiv]

 

2016
  • Moment Tensor Potentials: a class of systematically improvable interatomic potentials (A.V. Shapeev),  Multiscale Model. Simul., volume 14, pages 1153-1173, 2016. [bibtex] [pdf] [doi] [arXiv]

 

Presentations:

2021

    1. Making machine-learning potentials compatible with workflows for atomistic simulations. Workflows for atomistic simulations (A. Shapeev). Virtual (Bochum), March, 2021.
    2. Machine-learning interatomic potentials (A. Shapeev). CECAM/Psi-k workshop “Materials Design for Energy Storage and Conversion: Theory and Experiment”, Virtual, March, 2021
    3. From electrons to atoms to phase diagrams, all with machine learning (Highlight) (V. Ladygin, A. Shapeev). EUROMAT, September 2021.
    4. Machine-learning potentials for computational materials design (A. Shapeev). AI and machine learning in materials design, Seminar organized by FunMat-II (virtually in Linkoping). June 2021.
    5. Machine-learning interatomic potentials for automated prediction of materials properties (A. Shapeev). Machine Learning in Chemical and Materials Sciences, Santa Fe, USA, 2021.
    6. AI for accelerating and automating atomistic simulations (A.Shapeev), Russia-German Seminar “Digital Materials”, Moscow, December 2021.
    7. Mathematical sketches of machine-learning interatomic potentials and open problems (A. Shapeev). SIAM MS21 conference, May 2021.
    8. Machine learning for models of interatomic interaction (A. Shapeev). Actual Problems of Applied Mathematics, Novosibirsk University, Russia, October 2021.
    9. Moment tensor potentials for seamlessly accelerating first-principles calculations (A. Shapeev). 20th ONLINE USPEX workshop, November 2021.
    10. Machine-learning interatomic potentials as computational technology (A. Shapeev). Theory of Condensed Matter Seminar, Cambridge, February 2021.
    11. Machine learning for models of interatomic interaction (A. Shapeev). Conference of Mathematical Centers, Sirius University Russia, August 2021.
    12. Machine-learning interatomic potentials as a reliable computational tool (A. Shapeev). Online Colloquium Materials Modelling IMWF, University of Stuttgart, June 2021.

2020

    1. Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles (Tsymbalov E., Fedyanin K.,​ Panov M)​. OpenTalks.AI 2020, Moscow, Russia, February 21, 2020.
    2. Machine-Learning Interatomic Potentials on the Way to High-Throughput Calculations (A. Shapeev). Materials Genome Engineering Forum, Sichuan, China, October 22, 2020. (invited talk)
    3. Machine Learning in Molecular Modeling (A. Shapeev). Science and Artificial Intelligence conference, Novosibirsk, Russia, November 14-15, 2020. (invited talk)
    4. Seamless acceleration of ab initio materials modeling with machine-learning interatomic potentials (A. Shapeev, Evgeny Podryabinkin, Konstantin Gubaev, Ivan Novikov, Tatiana Kostiuchenko). BiGmax Workshop 2020 on Big-Data-Driven Materials Science, Dusseldorf, Germany, June 15-16, 2020 (invited talk)
    5. Could machine-learned potentials be part of high-throughput materials design? (A. Shapeev). (Machine) learning how to coarse-grain, Flagship CECAM workshop, Mainz, Germany,  September 28-29, 2020
    6. Automatic acceleration of quantum-mechanical atomistic simulations via machine learning of interatomic interaction (A. Shapeev), Computer Modeling in Physics and Beyond, Moscow, October 12-16, 2020.
    7. Machine-learning interatomic potentials, an automated tool of accelerating ab initio materials modeling (A. Shapeev). 19th ONLINE USPEX workshop, Moscow, November 11-13, 2020.
    8. Machine Learning-Assisted Ab Initio Exploration of High-Entropy Alloys (A. Shapeev). MRS Fall Meeting, Boston, USA, November 27 – December 4, 2020.
    9. Active learning of surrogate interatomic potentials from large-scale simulations with application to dislocation motion in tungsten (M. Hodapp, A. Shapeev). MSE Congress (online), Germany, September 22–25, 2020.
    10. In operando active learning of interatomic interaction during large-scale simulations (M. Hodapp, A. Shapeev). 6th GAMM AG Data Workshop (online), Germany, October 20–21.

2019

    1. Machine Learning of Interatomic Interaction (Shapeev A.), Materials Science Colloquium, University of Stuttgart, Institute for Materials Science, November 26, 2019.
    2. Machine Learning-Assisted Acceleration of DFT without Machine-Learning Errors (Shapeev A.), 2019 Materials Research Society Fall Meeting and Exhibit, Boston, December 1-6, 2019.
    3. Crystal Structure Prediction With Machine Learning Interatomic Potentials (Podryabinkin E., Kvashnin A., Shapeev A., Oganov A.) From empirical to predictive chemistry symposium. XXI Mendeleev Congress on General and Applied Chemistry, September, 9-13, Saint-Petersberg, Russia, 2019.
    4. Machine-learning Interatomic Potentials (A. Shapeev, E. Podryabinkin, I. Novikov, T. Kostiuchenko), EUROMAT, Stockholm, September 3, 2019.
    5. Investigations of order-disorder phase transitions in High entropy alloys with the use of machine-learning interatomic potentials (T. Kostiuchenko, F. Körmann, J. Neugebauer, A. Shapeev), in Hands-on DFT and Beyond: High-throughput Screening and Big-Data Analytics, towards Exascale Computational Materials Science, University of Barcelona, Barcelona, Spain, August 26 – September 6, 2019.
    6. Gaussian Processes and Decorrelation Masks: a Way to Enhance Dropout Uncertainty Estimate (E. Tsymbalov, A. Shapeev, M. Panov), Machine Learning Summer School, Skoltech, Moscow, Russia, August 26 – September 9, 2019.
    7. Machine Learning for Crystal Structure Prediction (E. Podryabinkin, A. Kvashnin, A. Shapeev, A. Oganov), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
    8. Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for barrierless reactions: Application to S + H2 system (I. Novikov, A. Shapeev, Y. Suleimanov), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
    9. Lattice dynamics simulation using machine learning interatomic potentials (V. V. Ladygin, P. Yu. Korotaev, A. V. Yanilkin, A. V. Shapeev), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
    10. Elastic Strain Engineering of Diamond: Tracking Treasure Down (Tsymbalov E., Shi Z., Dao M., Suresh S., Li J., Shapeev A), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
    11. Machine Learning Interatomic Potentials (A. Shapeev), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
    12. On flexible Green function methods for atomistic/continuum coupling (M. Hodapp). Invited talk at the 5th ECCOMAS Young Investigators Conference (YIC), Krakow (Poland), September 1–6 2019.
    13. Two examples of matrix methods solving machine-learning problems in molecular modeling (A. Shapeev, E. Podryabinkin, K. Gubaev, T. Kostiuchenko), in the 5th International Conference on Matrix Methods in Mathematics and applications, Skoltech, Moscow, Russia, August 19, 2019.
    14. Efficient crystal defect simulations through atomistic/continuum coupling using boundary element techniques (M. Hodapp, G. Anciaux, W. A. Curtin). 4th International Symposium on Atomistic and Multiscale Modeling of Mechanics and Multiphysics (ISAM4), Erlangen (Germany), August 5–8 2019.
    15. Machine-learning Interatomic Potentials (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov), in  9th International Congress on Industrial and Applied Mathematics, Valencia, Spain, July 16, 2019.
    16. Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning (E. Tsymbalov, S. Makarychev, A. Shapeev, M. Panov), in 36th International Conference on Machine Learning Workshop on Uncertainty & Robustness in Deep Learning, Long Beach, California, USA, June 14, 2019. (remote)
    17. Combination of a machine-learning potential and a genetic optimization algorithm to discover Boron allotropes (E. Podryabinkin), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    18. Application of ring polymer MD and machine-learning potentials to chemical gas-phase reactions (I. Novikov), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    19. Application of on-lattice interatomic potentials to alloy stability investigation (T. Kostiuchenko), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    20. Convex hull construction for ternary alloys (K. Gubaev), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    21. Neural networks for elastic strain engineering (E. Tsymbalov), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    22. Active learning for automatic construction of machine-learning potentials (A. Shapeev), in “Application of Machine-Learning Interatomic Potentials in Materials Design” International Workshop, Skoltech, Moscow, Russia, June 6, 2019.
    23. Efficient crystal defect simulations through atomistic/continuum coupling using boundary element techniques (M. Hodapp, G. Anciaux, W. A. Curtin). International Conference on Adaptive Modeling and Simulation (ADMOS), Alicante (Spain), May 27–29 2019.
    24. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials (K. Gubaev, E. Podryabinkin, G. Hart, A. Shapeev), DPG Spring Meeting, Regensburg, 31 March – 5 April, 2019.
    25. Investigation of phase stability in high-entropy alloys with the use of machine-learning interatomic potentials (T. Kostiuchenko, A. Shapeev, F. Körmann, J. Neugebauer), DPG Spring Meeting, Regensburg, 31 March – 5 April, 2019.
    26. “Perfect crime” of machine-learning potentials (A. Shapeev, K. Gubaev, E. Podryabinkin, G. Hart), APS Meeting, Boston, 4 March, 2019.

2018

    1. Deep Elastic Strain Engineering for Optimization and Exploration of Semiconductors Electronic Properties (E. Tsymbalov, Z. Shi, A. Shapeev, J. Li), in III International Workshop on Electromagnetic Properties of Novel Materials, Skoltech, Moscow, Russia, December 18-20, 2018.
    2. Elastic Strain Engineering Reaches Six Dimensions via Machine Learning (Z. Shi, E. Tsymbalov, A. Shapeev, J. Li), in 2018 Materials Research Society Fall Meeting, MIT, Boston, US, November 27, 2018.
    3. Study of multicomponent alloys using machine-trained interatomic potentials (T. Kostiuchenko, A. Shapeev, J. Neugebauer, F. Kormann), in 61st All-Russian MIPT Scientific Conference, Dolgoprudny, Russia, November 19-23, 2018.
    4. Machine-learning interatomic potentials (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov, T. Kostiuchenko), in “Ab initio Description of Iron and Steel”, Ringberg Castle, Germany, November 5-9, 2018.
    5. High-entropy alloys investigation using machine-learning potentials (T. Kostiuchenko, A. Shapeev, J. Neugebauer, F. Kormann), in “Multiscale Materials Modelling 2018″, Osaka, Japan, October 28 – November 2, 2018.
    6. Machine-Learning Interatomic Potentials: Progress, Application to Crystal Structure Prediction, and Mathematical Challenges (A. Shapeev) in Oberwolfach Workshop “Emergence of Structures in Particle Systems: Mechanics, Analysis and Computation”, Oberwolfach, Germany, October 29-November 2  2018
    7. Machine Learning Elastic Strain Engineering  (E. Tsymbalov, A. Shapeev, J. Li, Z. Shi) in 3rd Annual MIT-Skoltech Conference: “Collaborative Solutions for Next Generation Education, Science and Technology”, Moscow, Russia, October 15-16, 2018.
    8. Deep Active Learning: Gaussian Processes to the Rescue! (E. Tsymbalov, R. Ushakov, A. Shapeev, M. Panov)  in 3rd Annual MIT-Skoltech Conference: “Collaborative Solutions for Next Generation Education, Science and Technology”, Moscow, Russia, October 15-16, 2018.
    9. Application of machine-learning potentials to high-entropy alloys investigation (T. Kostiuchenko, A. Shapeev) in International Conference on Computer Simulation in Physics and beyond, Moscow, Russia, September 24-27, 2018.
    10. Active learning for the problems of computational material science (E. Tsymbalov, A. Shapeev, M. Panov) in International Conference on Computer Simulation in Physics and beyond, Moscow, Russia, September 24-27, 2018.
    11. Machine learning potentials: expanding the power of computational materials discovery (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov) 15th USPEX Workshop, Skoltech, Moscow, Russia, September 19, 2018
    12. Dropout-based Active Learning for Regression (E. Tsymbalov, A. Shapeev, M. Panov) 7th International Conference – Analysis of Images, Social networks and Texts, Moscow, Russia, July 5-7, 2018.
    13. Crystal Structure Prediction using Machine Learning Potentials (E. Podryabinkin, E. Tikhonov, A. Shapeev, A. Oganov) X International Scientific Conference “Kinetics and Mechanisms of Crystallization. Crystallization and materials of the future”, July 1-6, 2018.
    14. Dropout-based Active Learning for Regression (E. Tsymbalov, M. Panov, A. Shapeev) in 7th Symposium On Conformal & Probabilistic Prediction With Applications (COPA 2018), Maastricht, Netherlands, June 11-13, 2018.
    15. Machine Learning and Multiscale Modeling (K. Gubaev, E. Podryabinkin, I. Novikov, A. Shapeev) in All-Russian conference with international participation “Solid State Chemistry and Functional Materials”, Saint Petersburg, Russia, May 21-27, 2018.
    16. Active Learning of Linear Parametrized Interatomic Potentials (E. Podryabinkin, A. Shapeev) in All-Russian conference with international participation “Solid State Chemistry and Functional Materials”, Saint Petersburg, Russia, May 21-27, 2018.
    17. Machine Learning Interatomic Potentials (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov) in All-Russian conference with international participation “Solid State Chemistry and Functional Materials”, Saint Petersburg, Russia, May 21-27, 2018.
    18. Simultaneous exploring and learning PES (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov) in ACS National meeting 2018, New Orleans, USA, 19 March, 2018
    19. Machine Learning Interatomic Potentials for Multicomponent Systems (K. Gubaev, A. Shapeev) in Second Physics Informed Machine Learning Conference, Santa Fe, USA, January 21-25, 2018.

2017

    1. Simultaneous Learning and Exploring Atomistic Potential Surfaces: Current Progress and Mathematical Challenges (A. Shapeev) in Complex High-Dimensional Energy Landscapes, Los Angeles, USA, October 30 – November 3, 2017.
    2. Machine Learning and Multiscale Modeling (K. Gubaev) in Second All-Russian Scientific and Practical Conference of Young Scientists, Moscow, Russia, October 25-26, 2017.
    3. Material strain optimization meets machine learning (E. Tsymbalov, Shi Z., Shapeev A., Li J.) in Get-Y: Skoltech Young Scientist Cross-Disciplinary Conference, Shochi, Russia, September 27 – October 1, 2017.
    4. Machine learning-based interatomic potentials (A. Shapeev) in Multiscale theory and computation, Minneapolis, Minnesota, USA; September 23–25, 2017.
    5. Machine learning in Molecular Modelling (A. Shapeev, E. V. Podryabinkin, K. Gubaev) in Modern problems of continuum mechanics and explosion physics, Novosibirsk, Russia; September 4–8, 2017.
    6. Application of machine-learning interatomic potentials for oxide and cathode materials (I. Novikov, A. Shapeev) in 14th Russian Symposium “Foundations of  Atomistic Multiscale Modeling and Simulation”; August 16–27, 2017.
    7. Machine learning interatomic potentials for multicomponent systems (K. Gubaev, A. Shapeev) in 14th Russian Symposium “Foundations of  Atomistic Multiscale Modeling and Simulation”; August 16–27, 2017.
    8. Machine learning for approximation of electronic band structure of silicon (E. Tsymbalov, A. Shapeev) in 14th Russian Symposium “Foundations of  Atomistic Multiscale Modeling and Simulation”; August 16–27, 2017.
    9. Machine learning for construction of interatomic potentials (A. Shapeev, E. V. Podryabinkin, K. Gubaev, I. Novikov) in 14th Russian Symposium “Foundations of  Atomistic Multiscale Modeling and Simulation”; August 16–27, 2017.
    10. Active learning of linearly parametrized interatomic potentials (E. V. Podryabinkin, A. Shapeev) in 14th Russian Symposium “Foundations of  Atomistic Multiscale Modeling and Simulation”; August 16–27, 2017.
    11. Machine learning models of interatomic interaction (A. Shapeev, E. V. Podryabinkin, K. Gubaev) in The 11th International Conference on Large-Scale Scientific Computations, Sozopol,  Bulgaria; June 5–9, 2017.
    12. Machine learning strain engineering (Tsymbalov E., Baymurzina D., Shapeev A.) in 2nd Annual MIT-Skoltech Conference: “Shaping the Future: Big Data, Biomedicine and Frontier Technologies“, Moscow, Russia, April 25-26, 2017.
    13. Making machine learning interatomic potentials accurate, efficient, and reliable (A. Shapeev) in APS March Meeting, New Orleans, Louisiana, USA; March 13–17, 2017.

2016

    1. Toward accurate, efficient, and reliable interatomic potentials (A. Shapeev, E. Podryabinkin) in workshop “Collective Variables in Quantum Mechanics”, UCLA, Los Angeles, USA, November 14-18, 2016.
    2. Regression of interatomic interaction models from quantum mechanics (A.V. Shapeev), in Workshop “Uncertainty Quantification in Inverse Modelling”, Novosibirsk, April 25-27, 2016.
    3. A class of systematically improvable potentials (A.V. Shapeev, E.V. Podryabinkin), in Workshop “Physics Informed Machine Learning”, Santa Fe, January 19-22, 2016.

2015

  1. Construction of Accurate and Efficient Interatomic Potentials (A.V. Shapeev, E.V. Podryabinkin), in “Aircraft Engins of 21th century”, Baranov Central Institute of Aviation Motor Development, November 24-27, 2015.
  2. A class of systematically improvable potentials (A.V. Shapeev, E.V. Podryabinkin), in the International Symposium and Workshop “Electronic Structure Theory for the Accelerated Design of Structural Materials”, MISiS, Moscow, October 26-30, 2015.
  3. Perspectives of low rank approximation in molecular modelling (A.V. Shapeev), in “Matrix Methods and Applications”, Skoltech, Moscow, August 24-29, 2015.
  4. Construction of accurate and efficient interatomic potentials for molecular modelling (A.V. Shapeev), Workshop on “Multiscale Modeling and Analysis in Materials Science”, Shanghai Jiao Tong University, August 6-8, 2015.