Talk Recordings

Neural networks learning quantum chemistry

Olexandr Isayev

Scalable Methods for Molecular Property Prediction

Jonathan Godwin

Unifying machine learning and quantum chemistry with deep neural networks

Kristof Schütt

Ultra-Fast Interpretable Machine-Learning Potentials

Matthias Rupp

Active learning of ML potentials with uncertainty differentiation and attribution

Rafael Gómez-Bombarelli

More data or better data? How the training data influences machine learned predictions in Chemistry

Luis Itza Vazquez-Salazar

Δ-quantum ML potentials in realistic drug-like space

Jose Jimenez-Luna

Investigating Electrostatic Interactions with Data-Driven Models

Philip Loche

Machine Learning for Molecular Spectra and Solvent Effects

Michael Gastegger

(Degeneracy in) Symmetry-Preserving Neural Networks

Tess Smidt

Many-body message passing networks

Gabor Csanyi

Addressing the Challenge of Drug Discovery with Machine Learning and Exascale Computing

Alexandert Wade

Teaching free energy calculations to learn

John Chodera

Bespoke Interaction Potentials for Computer-Aided Drug Design

Daniel Cole

Rational discovery of cardiolipin binders by multiscale modeling and machine learning

Tristan Bereau

Deep learning for molecular modeling at Microsoft Research

Rianne van den Berg

Predicting protein-membrane interfaces of peripheral membrane proteins

Alexios Chatzigoulas

Building a Continuous Representation of Atomic Environment

Olga Kononova