Machine Learning

Pillar 2 — Machine Learning Platform for Molecules and Materials. This pillar aims to further strengthen the leadership of the Swiss research community in the use of machine learning to computational materials design.


Since the start of NCCR MARVEL, machine-learning (ML) techniques have become an integral part of the atomistic simulation toolbox, extending the reach and improving the accuracy of chemical modeling and driving materials discovery to new frontiers. MARVEL has made pioneering contributions to this field, ranging from the most fundamental understanding of the mathematical structure of descriptors used in atomistic ML, to challenging applications to basic chemistry and to materials of great technological relevance.


In the third and last phase of the NCCR, our ambitious research plan further bolsters our leadership in the application of ML to computational materials design.  It is built around a core infrastructural effort that both harmonizes the constellation of software tools developed during MARVEL's phase I and II and ensures a long-lasting legacy for the NCCR, keeping a forward-looking perspective. The final phase will allow us to continue advancing the state-of-the-art in atomistic machine learning, and integrate new PIs who will help deepen our understanding of the fundamental nature of atomistic machine learning and draw bridges with the study of quantum materials.


The project is led by Clémence Corminboeuf and Michele Ceriotti.

Group Leaders

Clémence Corminboeuf
Project leader
EPFL, Lausanne
Michele Ceriotti
Project leader
EPFL, Lausanne
Lenka Zdeborová
Group leader
EPFL, Lausanne
Giuseppe Carleo
Project leader
EPFL, Lausanne

Members

Sanggyu Chong
Postdoc, Pillar 2
EPFL, Lausanne
Giovanni Piccioli
PhD student, Pillar 2
EPFL, Lausanne