A rooted knowledge and understanding of the material and its properties stems from a holistic perspective. Indeed, when discussing the properties of a newly engineered material, it is common to present:

• a text-based description of the sequence of actions through which such material was obtained, listing key variables as scalars.
• a characterization of its structure by means of advanced microscopy (e.g., 2D images, 3D tomographies, 4D spatio-temporal analysis) and spectroscopy (e.g., adsorption spectra, NMR spectra), also with the aid of atomistic and electronic structure simulations.
• a list of key performance indicators, in the form of scalar variables (e.g. the mechanical properties of an alloy or the Seebeck coefficient of a thermoelectric) or a time-series (e.g., activity of a catalyst over time, the capacity of a battery over time).
• a mechanistic discussion of the relationships that link structure-to-property, often through quantities extracted from electronic structure and atomistic scale simulations.

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:

• Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
• Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

The event attendance is free of charge thanks to the generous support of the TAILOR EU network, Journal of Artificial Intelligence, MAX CoE, CECAM, PSI-K.

The workshop focus is placed on exploring innovative machine learning approaches, which exploit information and data of different nature (text, images, spectra, scalars, tensors) and from different sources of different fidelity, in the context of materials design and discovery. 

The first part of the workshop entails pedagogic intro lecture and hands-on tutorial, where researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods to tackle outstanding challenges across materials design and discovery.

In the second part of the workshop, invited and contributed speakers will delve on the state-of-the-art in machine learning for materials design, and discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem.

Confirmed Speakers include : 
Kevin Jablonka (FSU Jena, DE), 
Emma King-Smith (University of Cambridge, UK),
Christoph Koch (Humboldt-Universität zu Berlin, DE), 
Teodoro Laino (IBM Zurich, CH), 
Nataliya Lopanitsyna (Syngenta, CH), 
Jörg Neugebauer (Max Planck Institute, DE), 
Lilo Pozzo, (University of Washington, US)
Helge Stein (Technical University of Munich, DE)
Tian Xie (Microsoft Research, UK)

We look forward welcoming you in Ljubljana,
The organizing committee
Kevin Rossi (TU Delft), Milica Todorovic (University of Turku), Patrick Rinke (Aalto University), Stefano De Gironcoli (SISSA), Sintija Stevanoska (Jožef Stefan Institute), Sašo Džeroski (Jožef Stefan Institute)

The event is co-organized by the DAEMON COST Action CA22154 - https://www.cost.eu/actions/CA22154/ 

Registrations and Deadlines

Event registration takes place on  https://ml4ms.ijs.si/ , where additional details about the event are also available. We encourage interested participants to submit their abstracts for poster and oral presentation by February 29th, 2024. 

Abstract submission: 29.02.2024
Notification: 14.03.2024