Publications and Conferences

Papers:

  1. Gubaev, K., Podryabinkin, E. V., Hart, G. L., & Shapeev, A. V. (2019). Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Computational Materials Science, 156, 148-156. [doi] [arXiv]
  2. 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, 2018, [doi] [arXiv]
  3. Improving accuracy of interatomic potentials: more physics or more data? A case study of silica (I.Novikov, A.Shapeev), Materials Today Communications, 2018, [doi] [arXiv]
  4. 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. [doi] [arXiv]
  5. 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]
  6. 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]
  7. 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]

Conferences and Workshops:

  1. 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.
  2. 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.
  3. 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.
  4. 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
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. Simultaneous exploring and learning PES (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov) in ACS National meeting 2018, New Orleans, USA19 March, 2018
  17. 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.
  18. 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.
  19. Machine Learning and Multiscale Modeling (K. Gubaev) in Second All-Russian Scientific and Practical Conference of Young Scientists, Moscow, Russia, October 25-26, 2017.
  20. 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.
  21. Machine learning-based interatomic potentials (A. Shapeev) in Multiscale theory and computation, Minneapolis, Minnesota, USA; September 23–25, 2017.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. Making machine learning interatomic potentials accurate, efficient, and reliable (A. Shapeev) in APS March Meeting, New Orleans, Louisiana, USA; March 13–17, 2017.
  31. 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.
  32. Regression of interatomic interaction models from quantum mechanics (A.V. Shapeev), in Workshop “Uncertainty Quantification in Inverse Modelling”, Novosibirsk, April 25-27, 2016.
  33. A class of systematically improvable potentials (A.V. Shapeev, E.V. Podryabinkin), in Workshop “Physics Informed Machine Learning”, Santa Fe, January 19-22, 2016.
  34. 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.
  35. 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.
  36. Perspectives of low rank approximation in molecular modelling (A.V. Shapeev), in “Matrix Methods and Applications”, Skoltech, Moscow, August 24-29, 2015.
  37. 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.