Artificial intelligence in chemistry and materials science

The course includes lectures and practical work designed to reinforce the acquired knowledge both through joint review of program code with instructors and through independent implementation of algorithms aimed at solving current problems relevant to the course topic: predicting the properties of chemical compounds, optimizing molecular geometry, and designing new compounds.

 

Upon completion of the course, students will gain:

• basic knowledge of using the Python language for data analysis, as well as a general understanding of software code optimization and the use of graphics accelerators for handling large volumes of computations;
• an understanding of the specifics of working with chemical data and knowledge of chemical descriptors used to describe both molecular and crystal structures;
• knowledge of modern machine learning methods, global optimization and generation techniques, as well as various neural network architectures used in chemistry and materials science;
• independently written programs intended for training and using predictive and generative models that work with chemical data.

 

 

The course program includes 22 sessions: 8 lectures, 9 seminars, and 5 practical sessions

 

Requirements for students:

• basics of inorganic and organic chemistry at the level of understanding molecular and crystal structures;
• linear algebra and differential equations at the level of matrix and vector operations, as well as differentiation and integration of simple functions;
• programming basics at the level of understanding how to declare and use variables, functions, and data arrays.

 

 

Artem A. Mitrofanov

• Education

  Faculty of Materials Sciences
  MSU

• Areas of Expertise

  Машинное обучение в химии, квантовая химия, радиохимия

Artem A. Eliseev

• Education

  Faculty of Materials Sciences
  MSU

• Areas of Expertise

  Materials Science, computational solid state chemistry, software engineering

Cyrill V. Karpov

• Education

  Faculty of Physics
  MSU

• Areas of Expertise

  Machine learning in chemistry, chemoinformatics, computational chemistry

Vadim V. Korolev

• Education

  Faculty of Chemistry
  MSU

• Areas of Expertise

  Colloidal chemistry, machine learning in chemistry, materials science

Занятия проводятся на Химическом факультете МГУ им. М. В. Ломоносова

В программе курса 22 занятия: 8 лекций, 9 семинаров и 5 практикумов

Формат проведения: офлайн

Старт курса: с 7 сентября по четвергам с 12:30 до 14:05 в 559 аудитории второго ГУМа, 

по пятницам (четные недели) с 10:45 до 12:20 в 219 аудитории лабораторного корпуса Б.

Записаться на курс и задать вопросы можно по почте: aeliseev@inorg.chem.msu.ru (Артем Анатольевич Елисеев)

Страница курса на платформе Teach-in

 

Набор на курс 2023 года закрыт