In the last few years, machine learning and data-driven approaches, in synergy with established atomistic and molecular modeling methods, are enabling a paradigm shift in the accuracy and informativeness of computational predictions. [1, 2, 3] In this regard, we are moving from a moment of emergence to one of consolidation and maturity. This statement is corroborated not only in the remarkable growth of investigation exploiting machine learning methods, but also by the uptake of these methods in industries across the pharma, specialty chemicals, energy, and aerospace, as well as in the strong support from the market of start-ups promising the discovery and synthesis of materials and molecules with advanced properties thanks to artificial intelligence and physics-based modeling.

In this regard popularizing robust high-throughput workflows, established data analytics approaches, and machine learning-enabled realistic atomistic simulations pipelines to new generation of researchers approaching the field is expected to act as a level playing field and to accelerate discoveries and innovation in the domain of computational materials science, chemical physics, physical chemistry, and computational biophysics.

To this end, our events aims at providing a pedagogical introduction to young researchers, as well as established ones interested in rapidly adopting machine learning methods in their work. In particular, we have identified 8 topics that are nowadays commonly part of investigation concerning the understanding, characterization, or design of molecules, materials, and processes, namely:

1. Machine Learning accelerated High-throughput searches based on density functional theory (and beyond) simulations [4]
2. Bayesian optimization of materials and molecular properties [5]
3. Generative models for materials and molecules design [6]
4. Large language models and embeddings for materials and molecules property prediction [7]
5. Automated construction of machine learning interatomic potentials [8]
6. Non-adiabatic and excited state dynamics with machine learning models [9]
7. Learning coarse-grained models [10]
8. Integrating experimental data in machine-learning-driven materials discovery [11]

The first block concerns the exploration of large chemical spaces, generally accounting for equilibrium or ensemble averaged information, utilizing an array of methods, which ranges from materials and chemo-informatics approaches, to other closer to artificial intelligence. The second block of lecture reflects the key role of dynamical properties in determining the properties of a material, a molecule, or an experimental measurement. Also for this case, a diverse set of methodologies is considered, also in relation to a multi-scale vision.

The school will cover each of these topics through an introductory lecture and hands-on tutorial showcasing a realistic scenario. Importantly, the location of the school (Zadar, Croatia), is expected to enable the transfer of knowledge and cross-fertilization of network of researchers from the so-called (in the EU nomenclature) widening or inclusiveness countries in Europe.

Participants are strongly encouraged to submit an oral or poster contribution to the event by specifying this in the CECAM application form, accessible after clicking "Participate" on this webpage. In the application form, please include the contribution title and abstract in the "Your message" section. Several slots for oral contributions are available, and applicants will be informed about their acceptance for attendance and the type of contribution by April 17, 2025.

The school is free of charge. Travel and accommodation reimbursements are available for members of the COST DAEMON Action, in accordance with the official rules outlined in the Annotated Rules for COST Actions. Applicants intending to apply for this funding should clearly indicate this in their application form. To facilitate the organization of coffee breaks, refreshments, and the social dinner, we ask participants to also indicate any dietary restrictions in the application form.