Skolkovo Institute of Science and Technology
Skolkovo Institute of Science and Technology

Industry-Oriented Computational Discovery

Materials Science Machine learning "Smart" materials
Head of Laboratory

Kvashnin, Alexander Gennadyevich

DSc in Physics and Mathematics
Publications
100
Citations
5 700
h-index
31
CoLab ID
R-35BD6-0DEDB-YX90V
ORCID
0000-0002-0718-6691
ResearcherID
A-1825-2014
Scopus ID
6603118309
Google Scholar ID
6x6tbTYAAAAJ
Authorization required.
Lab website

The laboratory is engaged in conducting a computer search for new materials aimed at further use in the industry. The main directions are:

  1. Development of methods for predicting the properties of materials using AI
  2. Computer search for new materials with specified properties
  3. Investigation of the catalytic activity of new materials based on carbides, borides and nitrides of transition metals, including highly entropic compounds
  4. Prediction and research of new low-dimensional materials for use in catalysis (0D, 2D)
  5. High-entropy materials and alloys
  1. Computer search for materials
  2. Molecular dynamics and quantum chemical calculations
  3. Machine learning
  4. The evolutionary search for materials
  5. ai

Research directions

Catalytic nanoparticles

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Catalytic nanoparticles
Prediction and modeling of the structure and properties of catalytic nanoparticles of various compositions using both quantum chemical methods and AI

High-entropy materials

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High-entropy materials
Computer search for stable high-entropy carbides and borides of metals with subsequent experimental study of the obtained materials

Multicomponent materials

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Multicomponent materials
Prediction of stable crystal structures of multicomponent borides, carbides and nitrides of transition metals. The study of mixed compounds by methods of quantum mechanics, as well as using machine-learning potentials of interatomic interaction. Calculation of temperature stability and physical properties

Two-dimensional materials

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Two-dimensional materials
Prediction and study of the features of the atomic structure, physical, physico-chemical and catalytic properties of two-dimensional materials of various compositions.

Polycrystalline materials

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Polycrystalline materials
The study of the mechanical characteristics of polycrystalline and composite materials by atomistic modeling methods using machine-learning interatomic interaction potentials trained on the fly.

Tungsten Pentaboride

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Tungsten Pentaboride
Investigation of the physical, physico-chemical and catalytic properties of tungsten pentaboride (WB5-x) by theoretical and experimental methods.

Publications and patents

Found 
Александр Яковлевич Пак, Арина Андреевна Гумовская, Павел Вадимович Поваляев, Александр Геннадьевич Квашнин
RU2796134, 2023
Александр Геннадиевич Квашнин, Игорь Савельевич Любутин, Иван Александрович Троян, Дмитрий Владимирович Семенок, Артем Ромаевич Оганов
RU2757450C1, 2021
Вадим Вениаминович Бражкин, Василий Иванович Бугаков, Игорь Петрович Зибров, Владимир Павлович Филоненко, Артем Ромаевич Оганов, Александр Геннадьевич Квашнин, Артем Яудатович Закиров
RU2753339, 2021
Вадим Вениаминович Бражкин, Василий Иванович Бугаков, Игорь Петрович Зибров, Владимир Павлович Филоненко, Артем Ромаевич Оганов, Александр Геннадьевич Квашнин, Артем Яудатович Закиров, Андрей Александрович Осипцов
RU2698827, 2019

Partners

Tomsk Polytechnic University
Emanuel Institute of Biochemical Physics, Russian Academy of Sciences
Pirogov Russian National Research Medical University
Technological Institute for Superhard and Novel Carbon Materials
National University of Science & Technology (MISiS)

Lab address

Большой Бульвар 30, стр. 1
Authorization required.