Applied Mechanics Seminars

Computational Mechanics Seminars

Welcome to the regular seminars on current research topics in computational mechanics!

Presentations are given by invited lecturers from Skoltech as well as from outside to introduce students to current trends and advances in diverse areas of modern fluid and solid mechanics, applied mathematics, computational science, and industrial applications of mechanics. Students have the opportunity to learn from and interact with leading experts in computational mechanics and to enjoy exposure to cutting-edge topics and open problems in the field.

Seminars are held in English.

2025 seminar schedule is coming soon!

Lead Instructor: Aslan Kasimov, Associate Professor

Contacts: A.Kasimov@skoltech.ru

Project Center for Energy Transition and ESG

Master's Program in Applied Computational Mechanics

Aslan Kasimov, Associate Professor

FEBRUARY 5, 2:00 PM | MECHANICAL PROPERTIES OF SINGLE AND POLYCRYSTALLINE SOLIDS FROM MACHINE LEARNING

Location: R3-2009
Speaker: Faridun Jalolov, PhD student of Materials Science, Skoltech

Many industrial materials are synthesized as polycrystals or multiphase systems. They contain both a single crystal and amorphous components between single crystal grains. The large number of atoms makes it hard to calculate the properties of these systems using modern quantum-mechanical methods. Density functional theory can only be applied to materials with a few hundred atoms. To address the problem, we use a machine-learning approach based on Moment Tensor Potentials (MTP). As compared to other solutions, the potential of the new method learned in active learning on local atomic environments. When calculating a large structure with many hundreds of thousands of atoms, the MTP identifies which atom makes a mistake in the calculations, or is calculated incorrectly. The reason for this could be the limited training dataset, which prevents all possible system configurations from being considered. A local environment of this atom is then “cut out” and its energy calculated using quantum mechanics. Afterwards, the data is added back to the training set for further learning. As the on-the-fly learning progresses, the calculations continue until they come across another configuration that needs to be included in the training process. Other known machine-learning potentials cannot learn on small local parts of large structures, which limits their applicability and accuracy.

Google Scholar

ResearchGate

Mechanical Properties of Single and Polycrystalline Solids from Machine Learning (F. N. Jalolov, E. V. Podryabinkin, A. R. Oganov, A. V. Shapeev, A. G. Kvashnin)

Past seminars