Computational materials science · Skoltech
Skoltech Industry-Oriented Computational Discovery Lab
We discover new materials and predict their properties through advanced simulation, artificial intelligence, and close connection with real industrial challenges.
- Physics
- Chemistry
- Materials science
- Artificial intelligence
IOCD Lab at a Glance
Research Scope
- The use and implementation of AI-powered methods for predicting the properties of functional materials, in the broadest sense of the term
- Catalytic nanoparticles: metallic, bimetallic, high-entropy; DFT in conjunction with AI-powered techniques to construct predictive models
- High-entropy materials: MD simulations with ML potentials; AI-powered techniques for data processing
- High-temperature superconductivity under high pressure: stability; formation; properties; data analysis; ML-based predictive models
- Constructional and functional materials: MD simulations with ML potentials; analysis of degradation of properties; increasing the operating time
- Low-dimensional materials: thermal transport; electronic transport; new ML-based techniques
“We envision the future of materials science as a convergence of computational power, theoretical frameworks, and intelligent algorithms with experimental materials science”
Events
Upcoming Events
Past Events
International School for Young Scientists
Advanced Computational Materials Technologies
April 16, 2026 • Skolkovo Institute of Science and Technology
The school is dedicated to modern approaches in computational materials science, including atomic modeling and artificial intelligence. The event featured lectures by leading Russian and Chinese scientists, as well as presentations from young researchers.
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Courses
Thermodynamics of Materials
Graduate-level overview of thermodynamics applied to computational materials science.
Detailed Description
The course covers stability of materials, phase transitions, defects, surfaces, and thin films with applications in catalysis, sensing, and energy storage.
Computational Methods in Atomistic Simulations
Classical and modern computational approaches in materials science.
Detailed Description
The course spans classical transport theories to modern DFT, molecular dynamics, and machine learning methods with hands-on computational practice.