Detailed Speaker Schedule

14:00 – 15:20 Lilo Pozzo
Open-Science laboratory automation for AI-accelerated materials research and optimization
16:00 – 17:20 Brenden Pelkie
Open-Science laboratory automation for AI-accelerated materials research and optimization

09:00 – 10:20 Kevin Jablonka
Large language models for materials science
11:00 – 12:20 Kevin Jablonka
Large language models for materials science
14:00 – 15:20 Jörg Neugebauer
Maximizing High-Throughput Discovery and Machine Learning Efficiency Through Computational Workflows
16:00 – 17:20 Jörg Neugebauer
Maximizing High-Throughput Discovery and Machine Learning Efficiency Through Computational Workflows

09:00 – 10:20 Christoph Koch
Machine learning in electron microscopy and spectroscopy
11:00 – 12:20 Christoph Koch
Machine learning in electron microscopy and spectroscopy
14:20 – 15:00 Andy Sode Anker
Machine learning for analysis of experimental scattering data in materials chemistry
15:00 – 15:20 Andreja Benčan Golob
Addressing Challenges in 4D STEM Data of Ferroelectrics Using Machine Learning
16:00 – 16:20 Martin Uhrin
Symmetry-aware generative model for amorphous solids
16:20 – 16:40 Christer Söderholm
Materials design of inorganic crystals with 3D transformers
16:40 – 17:20 DAEMON talk

09:00 – 09:40 Ekin Dogus Cubuk
Scaling up computational materials discovery via deep learning
09:40 – 10:00 Javier Heras-Domingo
Unlocking the Potential of EXAFS: Machine Learning Approaches for Spectroscopic Data
10:00 – 10:20 Nejc Hodnik
Exploring Big Data for a Deeper Understanding of Electrocatalyst Behavior
11:20 – 11:40 Vinko Sršan
Deep learning-based drift correction in atomically resolved STEM images
11:40 – 12:00 Andrea Ruiz
Quantitative description of metal center organization and interactions in single-atom catalysts
12:00 – 12:40 Helge Stein
What comes after acceleration of research? What got us here?
14:00 – 14:20 Dušan Strmčnik
Machine Learning for Investigation of Nickel Surface Chemistry in Electrocatalytic Production of Hydrogen
14:20 – 14:40 Sašo Šturm
Autonomous laboratory for sustainable research and discovery of new materials
14:40 – 15:00 Austin Zadoks
Spectral Operator Representations
15:00 – 15:20 Franco Pellegrini
LATTE: an atomic environment descriptor based on Cartesian tensor contractions
16:00 – 16:20 Matthias Stosiek
Lignin Carbohydrate Complexes – Learning the Structure-Property Relation with Artificial Intelligence
16:20 – 16:40 Nataliya Lopanitsyna
Revealing Chemical Pathways in Reaction Data through Noctis
16:40 – 17:20 Emma King-Smith
Practical Machine Learning for Organic Small Molecule Modelling

09:00 – 09:40 Teodoro Laino
Fueling the Digital Chemistry Revolution with Language and Multimodal Foundation Models
09:40 – 10:00 Lei Zhang
A Comparative Study of Machine Learning Models and Vector Analysis Techniques for Improved Prediction of Quaternary Material Systems Based on Word Embeddings
10:00 – 10:20 Matej Praprotnik
Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media
11:20 – 11:40 Matilda Sipilä
Application of the Question Answering method to extract information from materials science literature
11:40 – 12:00 Morgan Kerhouant
Enterprise deployment, scaling and democratisation of R&D models
12:00 – 12:40 Tian Xie
MatterGen: a generative model for inorganic materials design