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Course title: Computational Chemistry and Materials Modeling (CCMM)

Class hours and location: Term 2, Tue/Wed/Fri 12:30-15:30, E-R2-2030

Course websitestorion.com/ccmm/syllabus

Course timeline: storion.com/ccmm/time

Course LMS site: Canvas, use it to

  • get nonpublic files such as lectures/notes (Files) and videos (Pages)
  • submit your works (Assignments), get feedback (Files) and see grades (Grades)
  • get announcements (Announcements)

Educational: code MA06008, 8 weeks by 20 hours, 6 ECTS, graduate level (MSc/PhD), max 10 students per TA, core course in Computational Materials Science track, the follow-up course is Advanced Materials Modeling

Course development: Sergei Tretiak and Andriy Zhugayevych, Dmitry Aksenov since 2017

Description: The course provides a graduate level overview of modern atomistic computer simulations used to model, understand and predict properties of technologically important materials. The emphasis is on practical use of techniques, algorithms and programs to bridge theory and applications, from the discovery of materials to their use in real-world technologies. Several laboratories give students direct experience with simulation methods, as well as practical knowledge on how to use computational modeling and how to present and interpret results of simulations. Bridges from atomic to complex systems demonstrate potential of different theories to applications relevant to multiple major industries in the future, including nanotechnology and energy.

Intended learning outcomes: At the end of the course, the students will be able to:

  • Understand fundamentals of modern Computational Materials Science at the level of atomistic modeling.
  • Apply fundamental knowledge about materials modeling via computer simulations, including terminology, key concepts, methods and topics of study.
  • Select range of computational methods appropriate for a given materials modeling study (balancing accuracy-feasibility trade-off) and identify wrong selections.
  • Use mainstream materials modeling software (MOPAC, Gaussian/FHI-aims, VASP/ABINIT, LAMMPS), including computations on HPC clusters.
  • Interpret and analyze results of simulations, including ability to identify wrong results and determine error bars.
  • Fully understand publications based on atomistic simulations.
  • Communicate orally and through writing results of simulations to materials scientists, including experimentalists.
  • For newly enrolled students, this course provides also a comprehensive information about research in Computational Materials Science conducted at Skoltech, with the opportunity to start research during the course.
  • For students already performing research in Computational Materials Science, this course also allow to refine and expand their set of relevant methods.

Assessment (see details here, see grading scheme):

  • 4/20=20% – final project (proposal + presentation + discussion + science + report)
  • 2/20=10% – final exam (presentation + answers on article-related question + answers on arbitrary questions)
  • 6/20=30% – 4 labs (submitted in two stages)
  • 4/20=20% – 3 homeworks
  • 4/20=20% – participation (attendance + questions in classes and quizzes)
  • 0-2/20 – extra points for optional team projects or final project progress reports (can be used to improve other grades)
  • For non-CMS students, requirements are 0.5-1 grade lower (because this is a core course for CMS students)
  • MSc and PhD students in CMS track are differentiated by amount and complexity of work
  • Any Lab can be redone after grading but with molecule/crystal given by Instructor (all such grades will be revised at the end of the course)
  • For each assignment, students will get a written or oral feedback except for: 'A'-graded works, 'B'-graded late submissions, submissions made after grading

Prerequisites: The course relies on strong undergraduate math/physics background, however, no background in computational chemistry is assumed or required. The general background in materials science is provided by Materials Chemistry course (Part I). See also Background literature and Required software


  • C J Cramer, Essentials of computational chemistry: theories and models (Wiley, 2004)
  • F Jensen, Introduction to computational chemistry (Wiley, 2007, 2017)
  • F Giustino, Materials Modelling using Density Functional Theory (OUP, 2014)
  • See the complete list here

Course content (see details here):

  1. Basics of quantum chemistry: Schrodinger equation for electrons, Born-Oppenheimer approximation, basis set.
  2. The ab initio many-body problem:from Hartree-Fock to wave function techniques.
  3. Density Functional Theory (DFT): applications and performance.
  4. ***Electronically excited states and theoretical spectroscopy. Polarizabilities, normal modes, vibrational spectra.
  5. Computational chemistry of molecules.
  6. Computational chemistry of crystals.
  7. ***Tight-binding and semiempirical approaches (including DFTB).
  8. Classical molecular dynamics (force fields, empirical potentials).
  9. Materials data science: Exploring materials space.
  10. Special methods co-developed by lecturers: MLIP, FHI-aims, ABINIT, NEXMD.

*** for self study