2025

The primary goal of the school is to provide young and established scientists (both at industry and academy) working at the intersection of explainable AI and Drug Discovery with a comprehensive overview of the most cutting-edge methods for predicting chemical reactivity, as well as a deep understanding of the remaining hurdles to be overcome in order for the field to advance further. Indeed, in the last few years Machine Learning (ML) has allowed to built upon large databases of chemical reactions to allow predictions of chemical reactions from given reactants, in a way similar to sentence translation. It is then possible to explore the chemical space by following reaction routes, to predict synthesis pathways or retrosynthesis pathways etc. Furthermore, an active field of ML for chemistry is the optimization of single step reaction conditions. The data in that case can be very different whether it comes from High Throughput Experiments (HTE), where 100's of thousands of reactions are performed automatically in a small chemical space and range of reaction conditions, or if it comes from a collection of standard laboratory works, like published works, that can be collected in databases such as the Open Reaction Database. In the latter case, the chemical space can be very large and the reaction conditions very broad and sometimes less documented, for example when using laboratory notebooks as source of information. Choosing the appropriate methods fitting the obejective pursued with the data at disposal is then of primary importance and this school will provide tools for students to do so. In parallel, progress has been made as well on the understanding of chemical reactions thanks to ML techniques. ML has been used to develop force-fields or potential energy surfaces, MLP's (Machine Learned Potentials) that have been leveraged to perform Molecular Dynamics simulations. This has led to the accurate description of reaction mechanics at the atomic level. At the same time, new methods in Artificial Intelligence (AI) have been developed to provide explanations along predictions. This so-called explanable AI can not only bring new information from the data but also reinforce the liability of ML, in particular when extrapolating from known data.

The coupling between electrons and phonons is at the heart of numerous physical phenomena and materials properties, from charge carrier transport to superconductivity and the temperature dependence of the electron energy bands and lifetimes to name a few [1]. Predicting and understanding these interactions is critical for the development of materials for new technologies, from optoelectronics to quantum technologies and spintronics. In recent years, there has been tremendous progress in the theoretical and computational modeling of electron-phonon interactions, driven by advances in first-principles theories, computational tools, and high-performance computing. It is time to bring together researchers working at the forefront of first-principles approaches to modeling electron-phonon processes in materials to discuss the state of the art, new ideas and developments from different codes, and some of the main open challenges.

Automated ab initio calculations have emerged as a powerful tool for computational materials science. Automated workflows offer many benefits over traditional manual approaches, including:

Reproducibility: Automation ensures a consistent calculation procedure for complex properties which often require many computational steps and the linking of multiple software packages.
Scalability: High-throughput computations enable wide-scale computational searches (often across tens of thousands of compounds) and the generation of large datasets that are essential for machine-learning.
Useability: Users benefit from the experience of domain experts with significant previous expertise calculating the properties of interest through well-tested default values and calculation procedures.