The MLIP package: Moment tensor potentials with active learning (I. Novikov). Psi-k workshop on Machine-Learning Interatomic Potentials, 2021. [YouTube]
Presentations:
2024
Machine-learned interatomic potentials for screening multi-component alloys (Ivan S. Novikov, Alexey S. Kotykhov, Alexander V. Shapeev, Max Hodapp). International Workshop on Data-Driven Computational and Theoretical Materials Design (DCTMD2024) October 9-13, 2024, Shanghai, China.
Machine learning in atomic modeling (Ivan Novikov). Colloquium of the HSE Faculty of Computer Science (online), September 24, 2024, Moscow.
Machine-learning force fields (and what we have learned while designing them) (Alexander Shapeev). Simposium of the NTI center at MSTU “Computational materials science: new materials and compounds”, April 2024, Moscow.
From Quantum Mechanics to Phase diagrams with AI (Alexander Shapeev). Current problems in materials strength, April 2024, Ekaterinburg.
From Quantum Mechanics to Phase diagrams with machine learning (Alexander Shapeev). Machine Learning in Chemical and Materials Sciences 2024 (virtual conference), May 2024, online.
Machine learning interatomic interaction and beyond (Alexander Shapeev). 2nd Sino-Russian Symposium on, Chemistry and Materials, May 2024, Moscow.
66th All-Russian Scientific Conference of MIPT, Moscow, Russia, April 2024:
Development of machine-learning potentials with explicit consideration of electrostatic interaction (D.V. Korogod, O.K. Chalykh, I.S. Novikov, A.V. Shapeev).
Construction of the phase diagram of the In-Au system with consideration of statistical error (Yarysheva I, Miryashikin T, Shapeev A, Kostiuchenko T).
Investigation of plasticity of metal alloys using machine-learning potentials potentials (Kolmakov A. A. , Rybin N., Shapeev A. V.).
Construction of the phase diagram of the Mg-Ca system with consideration for statistical uncertainty (Bartok D. E., Shapeev A. V., Mitryashkin T. N., Orekhov N. D.)
Implementation of an algorithm for automatic calculation of thermodynamic characteristics of single-component materials using machine-learning potentials (Spirande E., Shapeev A.)
31st International Scientific Conference for Undergraduate and Graduate Students and Young Scientists “Lomonosov”, Moscow, Russia, April 2024:
Construction of a phase diagram of the binary system In-Au (Yarysheva I).
Plasticity screening of metal alloys by atomistic modeling using machine-learning potentials (Kolmakov A. A.).
Construction of the phase diagram of the two-component Mg-Ca system (Bartok D. E.).
Implementation of an algorithm for automatic determination of thermodynamic properties of single-component materials (Spirande E.)
2023
Machine-learning potentials and digital labs of the future (Alexander Shapeev). Foresight-session “First All-Russia conference on computational materials science”, November, 2023, Moscow.
Artificial intelligence Methods for Quanititative Predicions of computationally complex properties of materials (Alexander Shapeev). AMTEXPO-2023, November 2023, Moscow.
Machine-learning force fields (Alexander Shapeev). Artificial Intelligence in Chemistry and Materials Science, December 2023, Moscow.
Machine-learning interatomic potentials for magnetic materials (I. Novikov, A. Kotykhov, M. Hodapp, C. Tantardini, A. Shapeev). V International Baltic Conference on Magnetism 2023 (IBCM 2023) (oral talk), Svetlogorsk, Russia, August 20-24, 2023.
Constrained DFT-based magnetic machine-learning potentials for magnetic alloys (I. Novikov, A. Kotykhov, M. Hodapp, C. Tantardini, A. Shapeev). International Conference “New Emerging Trends in Chemistry” (poster talk), Yerevan, Armenia, September 24-28, 2023.
Machine-learning models in on-lattice modelling (Kostiuchenko T.S., Bogdanov N.M., Shapeev A.V., Novikov I.S.). The Fifth International Baltic Conference on Magnetism (IBCM-2023), Kaliningrad, Russia, August 2023.
Machine-learning “on-lattice” models of interatomic interaction (Kostiuchenko T.S). Forsite-session, First All-Russian Conference in Computer Materials Science, Moscow, Russia, November 2023.
Magnetic machine-learning potential for magnetic alloys: a case study of Fe-A (A. S. Kotykhov, I. S. Novikov). V International Baltic Conference on Magnetism 2023.
Bayesian approach to the construction of phase diagrams (Miryashkin T., Klimanova O., and Shapeev A.). Forsite-session, First All-Russian Conference in Computer Materials Science (poster), Moscow, Russia, November 2023.
Chemical Vapor Deposition Research Using Machine Learning Potentials (K. Garifullin, I. Novikov, E. Podryabinkin, A. Shapeev, M. Medvedev). VIII All-Russian Conference on nanomaterials Nano-2023.
Exhaustive search for novel multicomponent alloys with brute force and machine learning (V. Zinkovich, V. Sotskov, A. Shapeev, and E. Podryabinkin). The First All-Russian Conference on Computer Materials Science, November, 2023.
65th All-Russian Scientific Conference of MIPT, Moscow, Russia, April 2023:
Application of magnetic machine-learning interatomic potential to study the Fe-Al compound (A. S. Kotykhov, A. V. Shapeev, I. S. Novikov).
A method for calculating the melting point of single-component materials based on molecular dynamics and Bayesian regression (Klimanova O., Shapeev A).
Chemical Vapor Deposition Research Using Machine Learning Potentials (K. Garifullin, I. Novikov, E. Podryabinkin, A. Shapeev, M. Medvedev).
Exhaustive search for novel alloys with brute force and machine learning (V. Zinkovich, E. Podryabinkin, and A. Shapeev).
2022
Accelerated materials informatics method for the discovery of ductile alloys (M. Hodapp, I. Novikov, O. Kovalyova, A. Shapeev). MSE Congress (poster talk), Darmstadt (DE), September 2022.
Making thermal rate constant calculations reliable using best practices: case study of OH + HBr → Br + H2O (I. Novikov, E. Makarov, A. Shapeev, Y. Suleimanov). XXIII International Conference on Chemical Thermodynamics in Russia (RCCT-2022, poster talk), Kazan, August 22-27, 2022.
From first-principles to phase diagrams: an example of artificial intelligence methods automating complex computational tasks (Vladimir Ladygin, Alexander Shapeev). Psi-k conference 2022, Lausanne, Aug 2022.
Ensuring Symmetry in Machine-learning potentials (A. Shapeev). APS March Meeting, March 2022 (delivered online).
Bayesian learning of ab initio phase diagrams: artificial intelligence deciding what to compute and with what model to compute (Vladimir Ladygin, Alexander Shapeev). 15th world congress on computational mechanics, August 2022 (delivered online).
Investigation of short-range order in multicomponent alloys with the use of machine-learning interatomic potentials (T.S. Kostiuchenko, F. Körmann, J. Neugebauer, A.V. Shapeev). XXIII international conference on chemical thermodynamics in Russia, RCCT-2022, Kazan, August 2022.
Development of machine learning potential on a lattice with magnetic degrees of freedom (Kostiuchenko T.S., Bogdanov N.M., Shapeev A.V., Novikov I.S.). XXIII All-Russian Conference of Young Scientists in Mathematical Modeling and Information Technology, Novosibirsk, Russia, November 2022.
Temperature-dependent phase transition in TiZrNbHfTaC5. A computational study (Sotskov V. E. Kvashnin A. G.). MSF2022 – The International Conference on Materials Science, April, 2022.
Prediction of new materials with a given crystal lattice (Sotskov V. E.).XXIII All-Russian Conference of Young Scientists on October 24-28 on mathematical modeling and information Technologies 2022.
2021
The MLIP package: Moment tensor potentials with active learning (I. Novikov), Psi-k tutorial workshop “ML-IP 2021” (invited talk), November 2021, online.
Making machine-learning potentials compatible with workflows for atomistic simulations. Workflows for atomistic simulations (A. Shapeev). Virtual (Bochum), March, 2021.
Machine-learning interatomic potentials (A. Shapeev). CECAM/Psi-k workshop “Materials Design for Energy Storage and Conversion: Theory and Experiment”, Virtual, March, 2021
From electrons to atoms to phase diagrams, all with machine learning (Highlight) (V. Ladygin, A. Shapeev). EUROMAT, September 2021.
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.
Machine-learning interatomic potentials for automated prediction of materials properties (A. Shapeev). Machine Learning in Chemical and Materials Sciences, Santa Fe, USA, 2021.
AI for accelerating and automating atomistic simulations (A.Shapeev), Russia-German Seminar “Digital Materials”, Moscow, December 2021.
Mathematical sketches of machine-learning interatomic potentials and open problems (A. Shapeev). SIAM MS21 conference, May 2021.
Machine learning for models of interatomic interaction (A. Shapeev). Actual Problems of Applied Mathematics, Novosibirsk University, Russia, October 2021.
Moment tensor potentials for seamlessly accelerating first-principles calculations (A. Shapeev). 20th ONLINE USPEX workshop, November 2021.
Machine-learning interatomic potentials as computational technology (A. Shapeev). Theory of Condensed Matter Seminar, Cambridge, February 2021.
Machine learning for models of interatomic interaction (A. Shapeev). Conference of Mathematical Centers, Sirius University Russia, August 2021.
Machine-learning interatomic potentials as a reliable computational tool (A. Shapeev). Online Colloquium Materials Modelling IMWF, University of Stuttgart, June 2021.
Application of a machine-learning interatomic potential to study the diffusion of point defects in uranium mononitride (A. S. Kotykhov, A. V. Shapeev, I. S. Novikov). 64th All-Russian Scientific Conference of MIPT, Dolgoprudny, 2021.
2020
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.
Machine-Learning Interatomic Potentials on the Way to High-Throughput Calculations (A. Shapeev). Materials Genome Engineering Forum, Sichuan, China, October 22, 2020. (invited talk)
Machine Learning in Molecular Modeling (A. Shapeev). Science and Artificial Intelligence conference, Novosibirsk, Russia, November 14-15, 2020. (invited talk)
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)
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
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.
Machine-learning interatomic potentials, an automated tool of accelerating ab initio materials modeling (A. Shapeev). 19th ONLINE USPEX workshop, Moscow, November 11-13, 2020.
Machine Learning-Assisted Ab Initio Exploration of High-Entropy Alloys (A. Shapeev). MRS Fall Meeting, Boston, USA, November 27 – December 4, 2020.
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.
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
Machine Learning of Interatomic Interaction (Shapeev A.), Materials Science Colloquium, University of Stuttgart, Institute for Materials Science, November 26, 2019.
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.
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.
Machine-learning Interatomic Potentials (A. Shapeev, E. Podryabinkin, I. Novikov, T. Kostiuchenko), EUROMAT, Stockholm, September 3, 2019.
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.
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.
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.
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.
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.
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.
Machine Learning Interatomic Potentials (A. Shapeev), in Inaugural Symposium for Computational Materials Program of Excellence, Skoltech, Moscow, Russia, September 4 – 6, 2019.
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.
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.
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.
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.
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
“Perfect crime” of machine-learning potentials (A. Shapeev, K. Gubaev, E. Podryabinkin, G. Hart), APS Meeting, Boston, 4 March, 2019.
2018
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Simultaneous exploring and learning PES (A. Shapeev, E. Podryabinkin, K. Gubaev, I. Novikov) in ACS National meeting 2018, New Orleans, USA, 19 March, 2018
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
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.
Machine Learning and Multiscale Modeling (K. Gubaev) in Second All-Russian Scientific and Practical Conference of Young Scientists, Moscow, Russia, October 25-26, 2017.
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.
Machine learning-based interatomic potentials (A. Shapeev) in Multiscale theory and computation, Minneapolis, Minnesota, USA; September 23–25, 2017.
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.
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.
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.
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.
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.
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.
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.
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.
Making machine learning interatomic potentials accurate, efficient, and reliable (A. Shapeev) in APS March Meeting, New Orleans, Louisiana, USA; March 13–17, 2017.
2016
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.
Regression of interatomic interaction models from quantum mechanics (A.V. Shapeev), in Workshop “Uncertainty Quantification in Inverse Modelling”, Novosibirsk, April 25-27, 2016.
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
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.
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.
Perspectives of low rank approximation in molecular modelling (A.V. Shapeev), in “Matrix Methods and Applications”, Skoltech, Moscow, August 24-29, 2015.
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.