Recorded Talks

Felix Faber – Representing Materials; an Overview and Covering New Ground

Matthias Rupp – Introduction to Learning with Kernels

Anton Bochkarev – Structural Descriptors and Atomic Cluster Expansion Basis

‪Milica Todorović‬ – Materials-structure Property Mapping with Kernel Based Methods

Alessandro Laio – Automatic Topography of Multidimensional Probability Densities

Kristof Schütt – Neural Networks for Materials Applications

Franco Pellegrini – Training Neural Network Potentials with PANNA

Boris Kozinsky – Symmetry and Uncertainty-aware Models of Interatomic Interactions for Fast Molecular Dynamics

David Peter Kovacs – Atomic Cluster Expansion based Force Fields for Molecular Materials

Ilyes Batatia – A Unified Understanding of Equivariant Interatomic Potentials

Robin Winter – Unsupervised Learning of Group Invariant and Equivariant Representations

Tuan Le – Unsupervised Representation Learning on Molecular Conformations

Yasemin Bozkurt Varolgüneş – Interpretable Embeddings from Molecular Simulations using Gaussian Mixture Variational Autoencoders

Saso Dzeroski – Learning Explainable Models from Complex Data with Applications in QSAR Modelling

Francesca Grisoni – Chemical Language Models for de Novo Molecule Design

Sebastiano Saccani – Industrial Applications of Generative Machine Learning Methods

Alessandro Laio, Francesca Grisoni, Kristof Schütt, Saso Dzeroski, Andrea Anelli, Julia Westermayr, moderated by Kevin Rossi – Panel Discussion 1

Jigasa Nigam – Towards Message Passing with Equivariant N-centered Representations

Julia Westermayr – Deep Learning for Excited States and Molecular Design

Lars Banko – Data-driven High-throughput Experimentation Using Combinatorial Material Science Methods and ML

I-Ju Chen – Precise Atom Manipulation Through Deep Reinforcement Learning

Sintjia Stevanoska – Relating the Composition and Mechanical Properties of Tungsten-based Composited by Using Machine Learning

Nataliya Lopanitsyna – Alchemical Machine Learning for High Entropy Alloys

Zhi Li – The Phase Diagram of Iron up to Earth’s Inner Core Conditions

Núria López – Machine Learning Techniques in Heterogeneous Catalysis

Johannes Margraf – Predicting Molecular Properties through Machine Learned Energy Functionals

Zachary Ulissi – Continued progress towards generalizable machine learning models in computational catalysis

Jarno Laakso – Compositional Engineering of Perovskites for Solar Energy Applications with Machine Learning

Jonathan Schmidt – Machine Learning Thermodynamic Stability of Materials

Felix Arendt – Evaluation of descriptors for property predictions of glasses by machine learning

Boris Kozinsky, Zachary Ulissi, Núria López, Rianne Van Der Berg, Lars Banko, moderated by Patrick Rinke – Panel Discussion 2

Claudio Zeni, Kevin Rossi with Matteo Carli and I-Ju Chen – Closing Remarks, Best Poster and Best Contributed Talk Prizes