This course comprises of two parts. The bases of the thermodynamics of equilibria and of the kinetics of reactions are introduced. The first notions of quantum chemistry on electrons and bonds, exemplified in organic chemistry, are presented in the second part.
The AI for Chemistry course will focus on teaching students how to use machine learning algorithms and techniques to analyze and make predictions about chemical data. The course will cover topics such as the basics of machine learning, common algorithms and their applications in chemistry.
This course will be on Electronic Laboratory Notebooks and is aimed at (future) users. Multiple electronic lab notebooks exists. The course will focus on the Cheminfo tools (https://eln.epfl.ch/).
Should have expertise in chemistry, physics or lite and material sciences. Although a very good knowledge in Al-based
algorithms is required to fully understand the technical details, a basic knowledge is sufficient to understand the potential
of these methods and their applications
This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.