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Administrative data

Philippe Schwaller

philippe.schwaller@epfl.ch https://schwallergroup.github.io

EPFL SB ISIC LIAC
CH J2 496 (Bâtiment CH)
Station 6
1015 Lausanne

EPFL SB ISIC LIAC
CH J2 496 (Bâtiment CH)
Station 6
1015 Lausanne

EPFL SB ISIC LIAC
CH J2 496 (Bâtiment CH)
Station 6
1015 Lausanne

EPFL SB ISIC LIAC
CH J2 496 (Bâtiment CH)
Station 6
1015 Lausanne

EPFL SB ISIC LIAC
CH J2 496 (Bâtiment CH)
Station 6
1015 Lausanne

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

PhD Students

Sandro Agostini, Daniel Paul Armstrong, Junwu Chen, Gabriel Gibberd, Salomé Guilbert, Anna Kelmanson, Sarina Nicole Kopf, Amin Mansouri, Andres Camilo Marulanda Bran, Cassandra Lynn Masschelein, Bohdan Naida, Rebecca Manuela Neeser, Théo Alain Neukomm, Nguyen Xuan Vu Nguyen, Víctor Sabanza Gil, Saumya Saumya, Rémi Michel Maxime Schlama, David Ming Segura, Wing Pong Sin, Paulo Filipe Valverde Das Neves, Susanna Di Vita

Past EPFL PhD Students

Jeff Guo (2025), Bojana Rankovic (2026)

Courses

AI for chemistry

CH-457

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.

AI in chemistry and beyond: Success stories

ChE-607

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.

AI in chemistry and beyond: Trends in the field

ChE-606

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.

AI in chemistry and beyond:Highlights in the field

ChE-605

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

PHYS-754

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.

Practical programming in Chemistry

CH-200

This course offers a comprehensive, practical introduction to computer programming tailored for chemists and chemical engineers. Python is the main language used throughout the course.

Project of Computational chemistry

CH-359

This course exploits modern computational tools in a research project aiming at resolving a chemistry problem by group of two students.