Duration
25h Th
Number of credits
| Master in chemistry, research focus | 3 crédits | |||
| Master in chemistry, teaching focus (Réinscription uniquement, pas de nouvelle inscription) | 3 crédits | |||
| Master in chemistry, professional focus | 3 crédits |
Lecturer
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course illustrates the synergy between different computational techniques (computational chemistry, data science, artificial intelligence) to understand and rationalize mechanisms and selectivities in synthetic organic chemistry.
The theoretical course is organized into six 2-hour modules:
- Module I - Introduction to Data Science
- Module II - Introduction to Computational Organic Chemistry
- Module III - In Silico Description of a Chemical Species
- Module IV - In Silico Description of a Molecular System
- Module V - Transition State theory
- Module VI - Artificial Intelligence in Chemistry
Tutorial sessions
A practical case study will accompany the course for a total of 13 hours. Students will apply the concepts of Modules III to VII through a progressive case study that will be integrated directly after each theoretical presentation. Throughout the term, each student will be required to complete, individually and outside scheduled class hours, the applied assignments set as part of the practical sessions (EPs). Their completion and quality will be assessed and taken into account in the final course grade (see assessment methods).
Laboratory work
There are no laboratory sessions associated with the CHIM0707 course.
Learning outcomes of the learning unit
Upon completion of the course, students will be able to:
- Use computational tools applied to physical organic chemistry
- Use computational chemistry preparation and visualization software (Gaussian, Gaussview, etc.)
- Describe and analyze a reaction in silico
- Understand the fundamental principles of physical organic chemistry
- Rationalize observed mechanisms and selectivities
- Relate theory to a practical case
Prerequisite knowledge and skills
Bachelor or/and Master background in organic chemistry, physical organic chemistry and Quantum Chemistry is necessary. The notions of coding acquired during the years of the Bachelor's degree in chemistry are also recommended.
Planned learning activities and teaching methods
- Interactive lectures, with presentation of the theoretical aspects and recent case studies from the literature (50%)
- Participatory learning, exercises based on a practical case (50%)
Mode of delivery (face to face, distance learning, hybrid learning)
Remote course
Further information:
Remotely via Teams.
E-learning options (exercises, additional resources).
Language of instruction
English
Course materials and recommended or required readings
Platform(s) used for course materials:
- MyULiège
Other site(s) used for course materials
- DOX (https://dox.uliege.be/index.php/s/cF1Og0W1XScnNWT)
Further information:
Platform(s) used for course materials:
- MyULiège
Additional information:
Lecture notes and lectures (in English, with audio commentary) are available via the myULiège and DoX platforms. Exercises and additional reading are suggested during the lectures.
Reference works:
- B. Foresman and A. E. Frisch, Exploring Chemistry with Electronic Structure Methods, 3rd ed., Gaussian, Inc.: Wallingford, CT, 2015.
ISBN: 978-1-935522-03-4 - Modern Physical Organic Chemistry, E. V. Anslyn, D. A. Dougherty, University Science Books, 2006 (ISBN 978-1-891389-31-3)
- Stereoelectronic effects, A. J. Kirby, Oxford University Press, 1996 (ISBN 978-0-198558-93-4)
- Modern Solvents in Organic Synthesis, P. Knochel (Ed.), Springer, 1999 (ISBN 3-540-66213-8)
- Computational Organic Chemistry, S. T. Bachrach, Wiley, 2014, 2nd ed. (ISBN 978-1118291924)
- Introduction to Machine Learning with Python: A Guide for Data Scientists, A. Müller, S. Guido, O'Reilly Media, 2016, 1rst edition (ISBN 978-1449369415)
- Data Science in Chemistry: Artificial Intelligence, Big Data, Chemometrics and Quantum Computing with Jupyter, T. Gressling, De Gruyter Textbook, 2021, 1rst edition (ISBN 978-3110629392)
- Recent literature (appropriate references will be delivered to illustrate the lectures)
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Continuous assessment
Further information:
Written open-book examination (open-ended questions)
Assessment
The course assessment comprises two components, each graded on a scale of 20:
- Written examination (60%): The examination is open-book and covers all course material. It includes questions assessing theoretical knowledge, understanding, and application.
- Continuous assessment (40%): This component covers the applied assignments completed individually throughout the term, outside scheduled class hours, as part of the practical sessions (EPs). The grade reflects completion of the assignments, the quality of the work, and the student's ability to apply the course concepts.
Calculation of the final grade: The weighted average, M, is calculated as follows: M = 0.60A + 0.40B, where A is the written examination grade and B is the continuous assessment grade.
Absorbing threshold rule: To pass the course, students must obtain at least 10/20 in each of the two components, A and B. If either component is below 10/20, the final grade is the lower of the weighted average, M, and 9/20. Failure in one component therefore cannot be compensated for by the other. If A = 10/20 and B = 10/20, the final grade is equal to M.
Example: A = 14/20 and B = 9/20 give M = 12/20; the final grade is therefore capped at 9/20, and the course is not passed.
Second examination session: The absorbing threshold rule also applies in the second examination session.
Any component graded below 10/20 must be retaken or remediated. An insufficient examination grade is replaced by the grade obtained in the new examination. If the continuous assessment grade is below 10/20, the student must complete an additional individual assignment in accordance with the instructions and deadline communicated by the course instructor. The grade obtained for this assignment then replaces grade B.
Any component graded at least 10/20 in the first examination session is automatically carried over to the second examination session. Students may nevertheless request to retake a component they have already passed; in that case, only the most recent grade is taken into account.
Work placement(s)
Nihil
Organisational remarks and main changes to the course
This course will be taught Ein nglish.
Students will receive a list of software to install prior to the course
Contacts
Contacts
- Dr. Pauline Bianchi
Département de Chimie, Bâtiment B6a
pauline.bianchi@uliege.be
- Prof. Jean-Christophe Monbaliu
Département de Chimie, Bâtiment B6a
jc.monbaliu@uliege.be