List of Speakers



Keynote Talks



Prof. Alexandre Tkatchenko, University of Luxembourg
Exploring Chemical Space Directly and Inversely


Prof. Steven Lopez, Northeastern University (US)
Machine-learning-accelerated photodynamics simulations in complex environments towards new materials and medicines


Prof. Heather Kulik, Massachusetts Institute of Technology (US)
Machine Learning for Open Shell Transition Metal Complex and Metal-Organic Framework Discovery


Prof. Yousung Jung, Seoul National University (KR)
Synthesis Predictions Enabled by Machine Learning


Prof. Fernanda Duarte, University of Oxford (UK)
Modelling Chemical Reactions in Solution with Machine Learning Potentials – Balancing Efficiency and Accuracy


Prof. Jacqueline Cole, University of Cambridge (UK)
Auto-generated Materials Databases and Language Models


Prof. Michele Ceriotti, EPFL (CH)
Hic sunt dracones - Uncertainty quantification for trustworthy exploration of chemical space



Invited Talks



Prof. Julia Westermayr, University of Leipzig (DE)
Chemical discovery assisted by machine learning




Prof. Milica Todorović, Turku University (FI)
Active learning for data-efficient optimisation of functional materials




Prof. Guido von Rudorff, University of Kassel (DE)
Symmetries and the Intrinsic Dimensionality of Chemical Space




Prof. Paul Popelier, University of Manchester (UK)
FFLUX




Prof. Klaus Robert Müller, TU Berlin (DE)
XAI meets chemistry




Prof. David Mobley, University of California, Irvine (US)
Using computation to design and explore compound libraries, with an emphasis on DELs




Prof. John Keith, University of Pittsburgh (US)
New paradigms for computationally interrogating atomic scale reaction mechanisms




Prof. Olexandr Isayev, Carnegie Mellon University (US)
Scaling Molecular Modelling to Millions of Reactions with AIMNet2 Neural Network Potential




Prof. Renana Gershoni-Poranne, Technion Israel Institute of Technology (IL)
Data is a Girl’s Best Friend: From High-Throughput Computations to Generative Deep Learning




Prof. Pavlo Dral, Xiamen University (CN)
AI simulations in chemical compound space




John Chodera, Memorial Sloan Kettering Cancer Center (US)
Teaching free energy calculations to learn





Invited Communication Talks



Prof. Julija Zavadlav, TUM School of Engineering and Design, Technical University of Munich
Deep Coarse-grained Molecular Modeling


Stefan Vuckovic, University of Fribourg, Switzerland
Transferability in machine-learned density functionals


Enrico Tapavicza, University Regensburg
Computational design of light-driven molecular nanomotors


Prof. Thijs Stuyver, Ecole Nationale Supérieure de Chimie de Paris, Université PSL
Hybrid computational workflows for reaction screening & discovery


Dr. Phillip Seeber, Friedrich Schiller University Jena
Spicy – A Functional Computational Framework for Multilayer Fragment Methods


Rebecca Manuela Neeser, EPFL (CH)
FSscore: A Personalized Machine Learning-based Synthetic Feasibility Score


Prof. Markus Meuwly, Basel University
Machine Learning-Based Potential Energy Surfaces for Molecular Simulation


Prof. Sergei Manzhos, Tokyo Institute of Technology
Reliable machine learning form sparse data in high dimension with additive kernel based methods


Dr. Rubén Laplaza, École Polytechnique Fédérale de Lausanne (EPFL)
Navigating homogeneous catalyst landscapes


Bing Huang, Wuhan University
Approaching Exact Quantum Chemistry through Data-Driven Multi-scale and Multi-fidelity Machine Learning Models


Dr. Lukas Hörmann, University of Warwick, Coventry (UK)
Atomic-scale insights into frictional energy dissipation mechanisms


Prof. Pascal Friederich, Karlsruher Institut für Technologie (KIT), Germany
MEGAN: Multi-explanation Graph Attention Network


Prof. François-Xavier Coudert, PSL University, Paris (FR)
Data-based methods to accelerate discovery of novel materials and better understand old ones


Prof. Christoph Bannwarth, Aachen University
MolBar: A Molecular Identifier for Inorganic and Organic Molecules with Full Support of Stereoisomerism


Prof. David Balcells, University of Oslo
Generative Machine Learning in the Transition Metal Compound Space


Dr. Andy S. Anker, Department of Energy, Danish Technical University, DenmarkDepartment of Chemistry, University of Oxford, England
Machine learning for analysis of experimental scattering data in materials chemistry



Speed Talks



Jan Weinreich, École Polytechnique Fédérale de Lausanne (EPFL), National Center for Competence in Research-Catalysis (NCCR-Catalysis)
Cost-Informed Bayesian Reaction Optimization


Prof. Joshua Schrier, Fordham University, New York
Large Language Models are a Strong Baseline for Inorganic Synthesizability and Precursor Selection Prediction


Kai Riedmiller, Heidelberg University
KIMMDY - Kinetic Monte Carlo Molecular Dynamics


Kimberly J. Daas, Vrije Universiteit Amsterdam (NL)
The Next Generation of Møller-Plesset Adiabatic Connection Functionals for Non-Covalant Interactions