Syllabus

Course title: Computational Chemistry and Materials Modeling

Class hours and location: Term 2, Mon 9-12, Wed/Fri 12:30-15:30, room R3-C2-2009

Course Canvas site: skoltech.instructure.com/courses/2235 (use it to get lectures and notes, to submit homeworks and projects, to see approximate grades)

Educational: code MA06008, 8 weeks by 20 hours, 6 ECTS, graduate level (MSc/PhD), core course in Computational Materials Science track

Skoltech address: Main Campus at 30 Bolshoy Blvd, Blue Bldg at 3 Nobel St, see also public transportation

Instructor: Andriy Zhugayevych

Office: Blue Bldg 223b

Office hours: at class, Tuesday 9-12, or by appointment

Phone: 3343

E-mail: a.zhugayevych@skoltech.ru

Co-Instructors: Sergei Tretiak, Dmitry Aksenov

T.A.: Nikita Tukachev (office Blue Bldg 220 CEST CREI open space area)

IT support: Viktor Vysotskii (v.vysotskii@skoltech.ru)

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 opportunity to start a 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):

30-35% – final project
25-30% – 4 labs (submitted in two stages)
20-25% – 3 homeworks
15-30% – exam + participation + optional team projects (or final project progress reports)
For non-CMS students requirements are 0.5-1 grade lower (because this is core course for CMS students)
MSc and PhD students in CMS track are differentiated by amount and complexity of work

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 Survey of Materials course (Part I). See also Background literature and Required software. If you plan to improve your background beyond the required minimum see this list.

Textbooks:

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

Course content (see details here):

Schrodinger equation for electrons, Born-Oppenheimer approximation, basis set.
Hartree-Fock (HF) and post-HF wavefunction based techniques.
Density functional theory (DFT).
Excited states and spectroscopy.
Computational chemistry of molecules and solids.
Tight-binding, DFTB, semiempirical methods.
Classical molecular mechanics, force fields, empirical potentials.
Molecular dynamics, metadynamics, kinetics, Monte-Carlo simulations.