List of confirmed speakers
52 confirmed speakers
KEY SPEAKERS
- Alan Aspuru-Guzik | University of Toronto
TBD - Markus Reiher | ETH Zurich
Lifelong Machine Learning Potentials - Gábor Csányi | University of Cambridge
Foundation models for materials chemistry - Kieron Burke | University of California, Irvine
Machine Learning Density Functionals - Anatole von Lilienfeld | University Of Toronto/Vector Institute
Quantum Machine Learning - Roberto Car | Princeton University
Deep potential models for equilibrium and near equilibrium processes - Oleg Prezhdo | University of Southern California
Nonadiabatic Molecular Dynamics with Machine Learning - Alexandre Tkatchenko | University of Luxembourg
Navigating Chemical Compound Space with Machine Learning - Olexandr Isayev | Carnegie Mellon University
AIMNet2: Robust neural network potential for organic, element-organic - Mario Barbatti | Aix Marseille University, CNRS, ICR
Machine Learning Nonadiabatic Dynamics
INVITED | CONTRIBUTED
- Fang Liu | Emory University
Machine learning aided chemical discovery in the solution phase - Arthur Mar | University of Alberta
Discovery of Inorganic Solids with Desired Structure Motifs Guided by Machine Learning - Jason Hattrick-Simpers | University of Toronto
Tutorial on the use of LLMs in Science - Nong Artrith | Debye Institute for Nanomaterials Science
ML & XAS for Amorphous Materials - Y Z | University of Michigan
Unusual Dynamics of Tetrahedral Liquids Caused by the Competition between Dynamic Heterogeneity and Structural Heterogeneity - Wissam Saidi | NETL
Materials Modeling and Machine Learning - Volker Deringer | University of Oxford
Data-driven interatomic potentials for inorganic materials chemistry - Pavlo Dral | Xiamen University
From fast potentials for dynamics to learning dynamics - Reinhard Maurer | University of Warwick
Machine learning of electronic structure for molecular design - Matthew Carbone | Brookhaven National Laboratory
TBD - Rebecca Lindsey | University of Michigan
Explaining Performance of Physics-Informed Machine-Learned Interatomic Models - Julien Lam | CNRS
Exploiting constrained linear models for machine-learning interaction potentials - David Yaron | Carnegie Mellon University
Quantum chemical Hamiltonians as flexible and interpretable model forms for machine learning - Puck van Gerwen | EPFL
EquiReact: Equivariant Neural Networks for Chemical Reactions - Rohit Goswami | University of Iceland
Throwaway Gaussian Processes for Saddle Searches - Justin Smith | NVIDIA
TBD - Karel Berka | Palacky University Olomouc
MolMeDB - free database of molecules on membranes - Chiho Kim | GeorgiaTech
TBD - Jing Huang | Westlake University
DP/MM: a hybrid force field model for zinc-protein dynamics - Aditya Nandy | UCLA
Leveraging Community Knowledge to Forge a Path Forward for Transition Metal Complex and Metal-Organic Framework Design - Rose Cersonsky | University of Wisconsin–Madison
Data-driven approaches to chemical and materials science:the impact of data selection, representation, and interpretability - Ankur Chatterjee | Nicolaus Copernicus University in Toruń
Spatio-temporal dependancies of in-plane and cross-plane thermal transport proerties by hybrid architecture of CNN and RNN - Marivi Fernandez-Serra | Stony Brook University
Learning the exchange and correlation functional in DFT - Matthias Rupp | Luxembourg Institute of Science and Technology (LIST)
Thermal transport via machine-learning potentials - Jenna Pope | Pacific Northwest National Laboratory
Accelerating Atomic-scale Simulations of Molecules and Materials with Neural Network Potentials - Connor Coley | MIT
Molecular design and the intersection with synthesis - David Balcells | University of Oslo
Generative Machine Learning for Transition Metal Chemistry - Vignesh Kumar | Nicolaus Copernicus University in Toruń
The critical role of XC potential in DFT : Our Big data analysis into DFT - Chenru Duan | Microsoft
Diffusion models on sampling rare events in chemistry - Eva Zurek | University at Buffalo, SUNY
Machine Learned Interatomic Potentials for Binary Carbides Trained on the AFLOW Database - Alexander Shapeev | Skolkovo Institute of Science and Technology
From quantum mechanics to phase diagrams with machine learning - Tom Penfold | Newcastle Univeristy
Deep Neural Networks for X-ray Spectroscopy: Hero or Zero? - Noa Marom | Carnegie Mellon University
Applications of Machine Learning in First Principles Materials Simulations - Bakhtiyor Rasulev | North Dakota State University
Application of Mixture-type Descriptors in Machine Learning Modeling of Materials - Dmitry Zubarev | IBM Research
Foundational Models for Chemical Discovery - Joshua Rackers | Prescient Design / Genentech
Building Physics into AI for Drug Discovery - Maria Chan | Argonne National Laboratory
Theory-informed ML for Microscopy, Spectroscopy, and Scattering - Aurora Clark | University of Utah
ML in Chemical Separations - Connecting Reaction Stoichiometry with Solution Structure - Daniel Schwalbe-Koda | UCLA
Unifying Views on the Extrapolation Power of Machine Learning Potentials and Materials Thermodynamics - Richard Hennig | University of Florida
Accelerating Materials Discovery Through Deep Learning and Ultra-Fast Potentials - Avanish Mishra | LANL
Learning from ‘Small’ Data Using Physics-Based Descriptors - Zsuzsanna Koczor-Benda | University of Warwick
Machine learning-based molecular design for plasmonic nanosystems