Philippe Schwaller
EPFL SB ISIC LIAC
CH J2 492 (Bâtiment CH)
Station 6
CH-1015 Lausanne
Web site: Web site: https://www.epfl.ch/labs/liac/
EPFL SB ISIC-GE
CH j2 492 (Bâtiment CH)
Station 6
CH-1015 Lausanne
Web site: Web site: https://www.epfl.ch/schools/sb/scgc/
Biography
Philippe Schwaller joined EPFL as a tenure-track assistant professor in the Institute of Chemical Sciences and Engineering in February 2022. He leads the Laboratory of Artificial Chemical Intelligence, which works on AI-accelerated discovery and synthesis of molecules. Philippe is also a core PI of the NCCR Catalysis, a Swiss centre for sustainable chemistry research, education, and innovation. He belongs to a new generation of scientists with a broad set of skills – in his case, a combination of chemistry, materials science, computer science, and experimental research.Teaching & PhD
Teaching
Chemistry and Chemical Engineering
PhD Students
Chen Junwu, Guo Jeff, Marulanda Bran Andres Camilo, Neeser Rebecca Manuela, Rankovic Bojana, Sabanza Gil Víctor, Schilter Oliver Tobias, Valverde Das Neves Paulo Filipe,Courses
Advanced general chemistry I
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.
AI for chemistry
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.
Introduction to the ChemInfo ELN of ISIC
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/).
AI in chemistry and beyond:Highlights in the field
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
Lecture series on scientific machine learning
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.