EN_Digitale Visualisierung

Learn more and click here

to promote mathematical-chemical model skills

Digital visualization

Relevance for chemistry

Goal and implementation

Digital visualization

Influence on chemistry studies

Influence on chemistry studies

OVerview

In the next few years, the increasing digitalisation will gain importance in the chemical industry and research. Therefore, it will be highly relevant for chemistry students to be able to present skills in Data science , Data management and other digital techniques in addition to content knowledge and competences. Thus, central challenges in the transition from school to university are the extended and new requirements with regard to the change of the different representational levels in chemistry.

mathematization

visualization of chemical problems

programming

Back

mathematics

content knowledge

computer science

Data science is an interdisciplinary field that uses algorithms and methods to examine, process and use data to derive knowledge, recommendations for action, optimisations and forecasts for a wide range of areas. Machine learning plays an important role in data science in order to cope with the constantly growing amount of data to be analysed.

Data Science

OVerview

automation of production processes

Prediction of optimal synthetic routes

prediction of molecular properties

increased efficiency, conservation of resources

Back

digital laboratory journals and global data sharing

simulation of reactions and syntheses (keyword: retrosynthesis)

molecular modelling and simulations

The possibilities of using digital applications and data science for chemistry are varied and will lead to sustainable changes in the professional profile of chemists.

Relevance for chemistry

OVerview

Goal and implementation

Support the development of chemical ways of thinking and working in problem- and research-oriented teaching/learning situations

Overarching goal

Implementation

Development of concepts

Substantive goal

Learning basic skills and knowledge in data science

Back

OVerview

Back

Integration of databases and data sharing concepts into practical laboratory work

OVerview

Use of models for theory-experiment validations in laboratory internships

Programming skills in Python

Development of programming tasks for the integration of data processing and visualization

Cross-event tutorials on data processing and visualization (Jupyter Notebook App)

Development of specific concepts

Back

OVerview

The project is under development. A long-term goal the implementation of the concepts in several courses of the basic studies. In winter semester 2020/21, the first course with the Jupyter Notebook App was successfully piloted in the course "Mathematics for Chemistry Students I".

Click here to see an example.

Implementation

Back

OVerview

Click on the picture to see it in a larger version.

In this course, chemistry students learn, for example, how to graph titration data and how to determine equivalence and half-equivalence points. They can benefit from these skills in a variety of ways in advanced courses.

Insight into data processing with Jupyter Notebook

OVerview

In case of any questions you can contact us viachemtool@chemie.uni-goettingen.de

Alegra Selchow

Project Coordinator Dekanat der Fakultät für Chemie Tamannstraße 4 37077 Göttingen

Nele Milsch

Executive Consultant Dekanat der Fakultät für Chemie Tamannstraße 4 37077 Göttingen

Prof. Dr. Ricardo Mata

Project Manager Institut für Physikalische Chemie Tamannstraße 4 37077 Göttingen