AI for Science seminar with Michele Ceriotti, EPFL.
Overview
- Date:Starts 13 November 2025, 15:00Ends 13 November 2025, 16:30
- Seats available:40
- Location:
- Language:English
Zoom password: ai4science
The on-site event will be followed by fika in the Analysen coffee area (fika from 16:00-16:30).
Abstract:
Machine-learning techniques are often applied to perform ‘end-to-end’ predictions, making black-box estimates of a property of interest using only a coarse description of the corresponding inputs. In contrast, atomic-scale modeling of matter is most useful when it allows one to gather a mechanistic insight into the microscopic processes that underlie the behavior of molecules and materials.
In this talk I will critically discuss how, and to which extent, physical-chemical ideas can and should be integrated into a machine-learning framework aimed at simulating matter at the atomic scale. I will discuss how physical priors, such as smoothness or symmetry of the structure-property relations, are used to inform the mathematical structure of a generic ML approximation - an approach that has become ubiquitous in the field.
I will also discuss the emergence of unconstrained models that can directly learn physical constraints from large amounts of data, and that can outperform in speed and accuracy models that enforce physical constraints - showing however that care should be applied to use these models safely, so that the lack of built-in physics does not result into unphysical results.
I will present examples relying on PET-MAD, a lightweight unconstrained model that achieves competitive accuracy across the periodic table despite being trained on a relatively small dataset, and that incorporates many advanced capabilities such as uncertainty quantification and direct force estimation.

About the speaker:
TBA

Structured learning
This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.