Working group of Jun.-Prof. Dr. Julia Westermayr

Teaching

On this page, you will find a brief overview of the modules and courses in which Jun.-Prof. Dr. Julia Westermayr and her team are involved. Learn about the different areas of focus and find out more about current and past teaching projects.

Bachelor modules

Here, you get an overview over current teaching courses in the B.Sc. Chemistry program at Leipzig University.

Recommended for3rd semester
ResponsibleChair of Theoretical Chemistry
Duration1 semester
Module cycleEvery winter semester
Teaching formats
  • Lecture “Introduction to Theoretical Chemistry” (2 contact hours per week) = 30 hours of in-class instruction + 60 hours of independent study = 90 hours total
  • Laboratory course “Introduction to Theoretical Chemistry” (2 contact hours per week) = 30 hours of in-class instruction + 30 hours of independent study = 60 hours total
Workload5 ECTS = 150 total hours of work
ApplicabilityMandatory module in the Bachelor of Science in Chemistry
ObjectivesStudents understand the fundamentals of Theoretical Chemistry and master its methods and applications.
ContentNecessity of quantum theory, historical context, time-independent Schrödinger equation, electron in a potential well, harmonic oscillator, rigid rotor, hydrogen atom, qualitative aspects of multi-electron atoms, chemical bonding, molecular symmetry, molecular vibrations, Hückel MO theory, electronic structure and bonding properties of π-electron and all-valence-electron systems
PrerequisitesSuccessful completion of the module “Introduction to Physical Chemistry I” (13-111-0411-X)
ReferencesFurther literature references will be provided in the courses.
Awarding of creditsCredits are awarded upon successful completion of the module. Further details are specified in the examination regulations.

Master modules

Here, you get an overview over current teaching courses in the M.Sc. Chemistry program at Leipzig University.

Recommended for2nd semester
ResponsibleProfessorship for Theoretical Chemistry of Materials Design
Duration1 semester
Module cycleEvery summer semester
Teaching formats
  • Lecture with integrated exercise “Machine Learning: Fundamentals and Applications in Chemistry” (2 contact hours per week) = 30 hours of in-class time and 60 hours of self-study = 90 hours
  • Seminar “Machine Learning: Fundamentals and Applications in Chemistry” (1 contact hour per week) = 15 hours of in-class time and 45 hours of self-study = 60 hours
Workload5 ECTS = 150 total working hours
Applicability
  • M.Sc. Chemistry
  • M.Sc. Structural Chemistry and Spectroscopy
ObjectivesStudents gain an insight into the field of Artificial Intelligence and its applications in chemistry. Building on the theoretical foundations of modern machine learning methods, they apply these methods in the exercise component. As part of this, students receive an introduction to the Python programming language to enable them to use AI. Students complete the exercises through self-study. In the seminar, they deal with applications of the methods.
Content
  • Introduction to Artificial Intelligence: supervised learning, unsupervised learning, and reinforcement learning
  • Representing chemical systems for AI methods: How can we encode chemical systems so that AI can learn from them?
  • Regression: from linear regression and ridge regression to deep neural networks and their applications in chemistry
  • Big Data analysis: dimensionality reduction, clustering, and classification
  • Molecular and material design with generative models
  • Self-driven laboratories: state of the art and AI potential in chemistry
PrerequisitesBasic understanding of theoretical chemistry
ReferencesPavlo Dral: "Quantum Chemistry in the Age of Machine Learning"
Christopher M. Bishop: "Pattern Recognition and Machine Learning"
Ian Goodfellow, Yoshua Bengio and Aaron Courville: "Deep Learning"
Weitere Hinweise zu Literaturangaben in den Lehrveranstaltungen.
Awarding of creditsCredits are awarded upon successful completion of the module. Details are determined by the examination regulations.

Recommended for3rd semester
ResponsibleProfessorship for Theoretical Chemistry
Duration1 semester
Module cycleEvery winter semester
Teaching formats
  • Lecture "Advanced Methods in Theoretical Chemistry" (4 contact hours per week) = 60 hours of in-class time and 90 hours of self-study = 150 hours
Workload5 ECTS = 150 total working hours
Applicability
  • M.Sc. Chemistry
  • M.Sc. Structural Chemistry and Spectroscopy
  • M.Sc. Advanced Spectroscopy in Chemistry
ObjectivesStudents have knowledge of modern methods of theoretical chemistry and computational chemistry and are able to apply them to current research questions.
ContentMethods for analyzing chemical bonding in molecules, surfaces, and solids; methods for treating dynamic processes; kinetic Monte Carlo; advanced density functional theory methods; current research areas in theoretical chemistry and computational chemistry; new methodological developments. Applications to questions in atomic-scale processing. Fundamentals of density functional theory are assumed.
PrerequisitesNone
ReferencesLiterature references will be provided in the lectures.
Awarding of creditsCredits are awarded upon successful completion of the module. Details are determined by the examination regulations.

Recommended for3rd semester
ResponsibleProfessorship for Theoretical Chemistry of Materials Design
Duration1 semester
Module cycleEvery semester
Teaching formats
  • Practical course “Advanced AI in Theoretical Chemistry” (10 contact hours per week) = 150 hours of in-class time and 150 hours of self-study = 300 hours
Workload10 ECTS = 300 total working hours
Applicability
  • M.Sc. Chemistry
  • M.Sc. Structural Chemistry and Spectroscopy
  • M.Sc. Advanced Spectroscopy in Chemistry
ObjectivesThe aim of this advanced practical course is to give students their first insights into the application of machine learning methods for (theoretical) chemistry by means of independent scientific work. Students will be able to transform current issues in (theoretical) chemistry into AI problems and develop solution approaches using machine learning methods (supervised, unsupervised, and reinforcement learning).
Content
  • Generating training datasets with methods of theoretical chemistry (DFT, methods for excited states, semiempirical methods, etc.)
  • Regression problems: Learning relationships between structure and properties of molecules and materials (kernel ridge and Gaussian process regression, deep neural networks)
  • Classification problems: Gaining new insights through data analysis (dimensionality reduction methods, clustering, etc.)
  • Molecular and material design using generative models
PrerequisitesBasic knowledge of theoretical chemistry
ReferencesPavlo Dral: "Quantum Chemistry in the Age of Machine Learning"
Christopher M. Bishop: "Pattern Recognition and Machine Learning"
Ian Goodfellow, Yoshua Bengio and Aaron Courville: "Deep Learning"
Awarding of creditsCredits are awarded upon successful completion of the module. Details are determined by the examination regulations.

Additional/previous courses

Here, you can find an overview over additional and previous courses.

  • Winter semester 2023/2024
    PI tutorial on machine learning for the Research Training Group 2721, Leipzig University
    Module for doctoral students within the research training group (experimentalists and theoreticians) to learn how to use machine learning for their data. In 3 sessions, the students select their data, define research goals to be solved with machine learning and data analysis, receive hands-on-tutorials, and are given instructions on how to apply machine learning tools to their data. Doctoral candidates of the group support the hands-on-sessions and provide feedback.

  • Winter semester 2022/2023, 2023/2024, summer semester 2023, 2024
    Exercises “Artificial Intelligence in Theoretical Chemistry”, Leipzig University (10 SWS)
    Intense practical course for Master students to work on current research problems in the field of machine learning for theoretical chemistry. Full responsibility for the course.

  • Term 2, 2021 and 2022
    Pen and Paper Workshops for “Electrons in Molecules and Solids” (the lecture was awarded the Andrew McCamley teaching award of WarwickChem with Dan Murdock), University of Warwick
    This physical chemistry class taught chemical bonding theory in molecules and solids for 2nd year undergraduate students. 

  • Term 2, 2021
    Workshop, guest lecture and assignment for the course “Quantum Chemistry” on excited state methods, University of Warwick
    This course was aimed for PhD students of various disciplines as an introduction to computational quantum chemistry. I prepared and taught a 2-hour workshop on methods for excited states including hands-on exercises using Psi4 and prepared the assignment. 

  • Term 1 and 2, 2020
    Physical Chemistry Tutorials, University of Warwick
    These tutorials were for 1st year undergraduate students. The topics covered were basic quantum chemistry, reaction kinetics, spectroscopy, and thermodynamics.

  • Fall 2017 – summer 2020
    Laboratory course in theoretical chemistry, University of Vienna (4 SWS)
    This lecture introduced theoretical chemistry on the computer to 2nd year Bachelor students. I designed the lectures and the exams and was fully responsible for it, including the preparation, organization, and grading of the exams.
  • Fall 2019
    Machine learning for molecules and materials, University of Vienna (4 SWS)
    This combined lecture and exercise was introduced for Master and PhD students from the fields of chemistry, physics, computer science, etc. I was responsible for the exercises held in class on the computer and helped to design the lecture (topics and teaching techniques).
  • Fall 2017 and 2019
    Voluntary exercise in theoretical chemistry, University of Vienna (6 SWS)
    This exercise is for 2nd year Bachelor students. It should help them to better understand the theoretical concepts taught in the complementary lecture “Theoretical Chemistry” using blackboard exercises. In 2017, the exercises were fully voluntary for students and for lecturers, but the overly positive feedback led to the introduction of it as an official 3-credit course in 2019. Students nominated us for the Univie Teaching Award in 2019.

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