AI in Material Science
Head of the programme
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Head of the programme
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Programme structure
- The Master programme “AI in material science” comprises 120 credits (ECTS, Euoropean Credit Transfer System) and it is design to compete it with a total duration of 2 years. Apart from the common and mandatory courses the programme contains kit of courses that can be chosen by the students depending on their specialization, that makes possible to pass the programme by one of the following tracks:
- Engineering/Chemical track
- Data-driven material science
All courses might be formally divided into four groups:- Basis courses in physics and chemistry (i.e. “material science (physical aspects)”, “material science (chemistry)”, “X-ray methods for materials characterization” etc.)
- Specialized courses (i.e. “synchrotron-based techniques for materials characterizations”, “quantum chemistry simulations”, “synthesis of metal-organic frameworks”, “additive technologies for microfluidics” etc)
- Data analysis and artificial intelligence (i.e. “AI for ScienceX”, “AI for prediction of materials properties”, “LLM models in materials science”, “Data analysis and visualization” etc);
- Soft-skills and communications (i.e. “Academic writing”, “Research seminar”, “Technology transfer”, “Project managements” etc)
Head of the programme
Name
Head of the programme
Name
Choosing your study track (specialisation)
The master programme “AI in material science” offers two tracks:
A: Engineering/chemical track
B: Data-driven material science
The splitting into tracks basically occurs at the first semester, where students of each track offer to follow five unique specialized courses.
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Engineering/chemical trackAn engeenirng/chemical track assumes deeper specialization in the possible mechanism of automatization of chemical synthesis and/or processes (e.g. like catalysis), aimed to development of engeeniring solutions and generating of “chemical” ideas on the way of creation of autonomous or robotic systems for sustainable production of materials with desired or advanced properties. Among the specialized courses of track A are the following:- Modeling of microfluidic chips and their hydro-dynamics
- Robots and automatization in materials science
- Micro-tonnage chemical production
- Chemical synthesis automatization
- Patent technology
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Data-driven material scienceWhile the second track “Data-driven materials science” invoked in implementation of AI in solving actual and challenging problems in modern materials science, such as (i)computer vision technology for treatment and analysis of scientific related images/video; (ii) prediction of materials properties based on their structure on the atomistic level; (iii) advanced visualization and analysis of spectroscopic data – bringing the gap between spectral descriptors and materials properties. Among the specialized courses of track B are the following:- Datasets and their analysis
- Advanced data visualization and analysis
- Generic AI in modern material science
- LLM for material science applications
- NN and deep learning technologies for materials science
The programme is build in such way that student gradually getting familiar with theoretical knowledge of the main subject in physics and chemistry of materials, as well as in parallel start gaining some valuable practical skills in both materials’ synthesis and characterization techniques as well as understanding and application of different AI-based models to solve important and actual problems in modern materials science.
In more detail, the autump semester of the 1st year combines courses on the physical aspect of material science ("materials sceince (physics)") and statistic physics as well as data analysis ("statistic data analysis") and coding. Along with technical courses first semester offers development of some soft skills throught the courses such as academic writing in English as well as project management.
The 2nd semester continues some basic courses which aimed to provide some fundamental theoretical knowledge about how structure of materials might be related with their properties (such as “materials science (chemistry)”), and introduce an X-ray based method for materials characterization which makes students familiar with important and abundant families of techniques widely used in modern materials science. Gaining the knowledge in coding and AI is continued with “ML in materials science” and “AI for ScienceX” courses.
Finally, the 3rd semester contains mostly specializes courses, part of which are common for both tracks (e.g. “quantum chemistry simulations”, “robotic technology in materials science”, “AI for prediction of materials properties”) and major part will be delivered in accordance with the track of deep specialization chosen by students (Engineering or data analysis tracks).
While in the last quarter of the programme is mostly dedicated to the research activities of the students which are supposed to be their qualification work. At this stage we do believe our students have already gained important theoretical background and valuable practical skills in different topics and aspects and ready to apply them for solving topical and challenging problems in materials science and data analysis.
On preparing the master thesis our students working with really relevant tasks, which is confirmed by the fundings engaged on the different projects as well as the interest in collaboration from different leading institutions in Russia and industrial partners.
In more detail, the autump semester of the 1st year combines courses on the physical aspect of material science ("materials sceince (physics)") and statistic physics as well as data analysis ("statistic data analysis") and coding. Along with technical courses first semester offers development of some soft skills throught the courses such as academic writing in English as well as project management.
The 2nd semester continues some basic courses which aimed to provide some fundamental theoretical knowledge about how structure of materials might be related with their properties (such as “materials science (chemistry)”), and introduce an X-ray based method for materials characterization which makes students familiar with important and abundant families of techniques widely used in modern materials science. Gaining the knowledge in coding and AI is continued with “ML in materials science” and “AI for ScienceX” courses.
Finally, the 3rd semester contains mostly specializes courses, part of which are common for both tracks (e.g. “quantum chemistry simulations”, “robotic technology in materials science”, “AI for prediction of materials properties”) and major part will be delivered in accordance with the track of deep specialization chosen by students (Engineering or data analysis tracks).
While in the last quarter of the programme is mostly dedicated to the research activities of the students which are supposed to be their qualification work. At this stage we do believe our students have already gained important theoretical background and valuable practical skills in different topics and aspects and ready to apply them for solving topical and challenging problems in materials science and data analysis.
On preparing the master thesis our students working with really relevant tasks, which is confirmed by the fundings engaged on the different projects as well as the interest in collaboration from different leading institutions in Russia and industrial partners.
Programme News
Adding news notes related to this programme: photos, date of publication (with the ability to edit the date), text with the ability to insert images, videos, tables, links, upload documents
Rankings
The SFedU operates a point-ranking system for assessing students' knowledge during the ongoing control and interim assessment of students’ progress. Students can see their scores in their personal account. The ranking of students by points is taken into account when switching to a non-fee form of training.
Practical training
During their studies, students take a practical training in the form of a research project, an internship at a partner company, and a pre-diploma training, aimed at preparing their Master thesis.
State final attestation
The state final attestation is a public defense of the Master thesis before the commission. The commission includes teachers and representatives of business structures and other universities.
Student exchange
Student exchange
Master students can take part in exchange programmes in the 2nd or 3d semester. More https://sfedu.ru/www/stat_pages22.show?p=INT/N12598/P
Master students can take part in exchange programmes in the 2nd or 3d semester. More https://sfedu.ru/www/stat_pages22.show?p=INT/N12598/P
The information will be available soon.