Lebenslauf und Publikationen Dr. Kostiantyn Hubaiev

Dr. Kostiantyn Hubaiev (Konstantin Gubaev) is a Senior Researcher at FIZ Karlsruhe with Prof. Dr. Harald Sack. He received profound knowledge of physical and mathematical disciplines during his Bachelor, and while Master he worked closely with industry-oriented metal processing facilities. During his PhD he obtained a machine learning expertise and from that time onwards, was applying machine learning methods in conjunction with the first-principles methods, providing a new paradigm in materials science research. During his PhD he was among the main developers of the MLIP package for the Moment Tensor Potentials (https://mlip.skoltech.ru/) which is now used by numerous research groups worldwide. 
Education:

• 2015-2019 – PhD in Materials Science, Skolkovo Institute of Science and Technology
• 2013-2015 – Master in Physical Metallurgy, E.O. Paton Electrical Welding Institute
• 2009-2013 – Bachelor in Applied Physics and Mathematics, Moscow Institute of Physics and Technology

Work Experience:

• 2021-2023 – Postdoc researcher in University Stuttgart, Chemistry Department
• 2019-2021 – Postdoc researcher in TU Delft, Maritime Engineering Department
• October 2014 - April 2015 – Research assistant ISF RWTH Aachen, Simulation Department
• June 2011 – June 2012 Programmer in Moscow Institute of Physics and Technology, Department of Higher Mathematics

ORCID profile: https://orcid.org/0000-0003-2612-8515

2023

• In preparation: Study of dislocation motion in bcc Nb
• In preparation: Study of the mesoporous Silica

2022

• Gubaev, Konstantin, et al. "Performance of two complementary machine-learned potentials in modelling chemically complex systems." npj Computational Materials 9.1 (2023): 129.

2021

• Gubaev, Konstantin, et al. "Finite-temperature interplay of structural stability, chemical complexity, and elastic properties of bcc multicomponent alloys from ab initio trained machine-learning potentials." Physical Review Materials 5.7 (2021): 073801.
• Rosenbrock, C. W., Gubaev, K., Shapeev, A. V., Pártay, L. B., Bernstein, N., Csányi, G., & Hart, G. L. (2021). Machine-learned interatomic potentials for alloys and alloy phase diagrams. npj Computational Materials, 7(1), 24.
• Ikeda, Y., Gubaev, K., Neugebauer, J., Grabowski, B., & Körmann, F. (2021). Chemically induced local lattice distortions versus structural phase transformations in compositionally complex alloys. npj Computational Materials, 7(1), 34.

2020

• Novikov, I. S., Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2020). The MLIP package: moment tensor potentials with MPI and active learning. Machine Learning: Science and Technology, 2(2), 025002.
• Ferrari, A., Dutta, B., Gubaev, K., Ikeda, Y., Srinivasan, P., Grabowski, B., & Körmann, F. (2020). Frontiers in atomistic simulations of high entropy alloys. Journal of Applied Physics, 128(15).
• Shapeev, A. V., Podryabinkin, E. V., Gubaev, K., Tasnádi, F., & Abrikosov, I. A. (2020). Elinvar effect in β-Ti simulated by on-the-fly trained moment tensor potential. New Journal of Physics, 22(11), 113005.
• Shapeev, A., Gubaev, K., Tsymbalov, E., & Podryabinkin, E. (2020). Active learning and uncertainty estimation. Machine Learning Meets Quantum Physics, 309-329.

2019

• 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.

2018

• Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2018). Machine learning of molecular properties: Locality and active learning. The Journal of chemical physics, 148(24).

• July 2022 - July 2023: supervising Master student on his research project and thesis “Machine Learning Interatomic Potentials for amorphous mesoporous metallosilicates”
• March 2021 – November 2022: supervising Bachelor student on her research and thesis “Development of ab initio-based Machine-Learning Potentials for Amorphous Silica”
• February 2019 – June 2019 Team Assistant at “Neural Networks” course during PhD study. 
• September 2018 – December 2018 Team Assistant at “Technology Commercialization” course during PhD study

Music modelling algorithms, mobile development, active sports, nature contemplation