Role of Machine Learning in Molecular Discovery & Scientific Understanding
Keynote speaker: Max Welling | 4TU.HTM organizes a joint workshop integrating applications in material discovery into machine learning frameworks. Key challenges are the prediction of crystal structures, the discovery of stable functional molecules, and the rational material design process.
Join us exploring this future of scientific discovery!
The exponential growth of computational power combined with the emergence of innovative machine learning (ML) algorithms offers a revolutionary paradigm in the realm of molecular discovery and scientific understanding. This workshop aims to create a dialogue between pure ML researchers, and scientists using cutting-edge models for data-centric scientific molecular and material discovery.
Register now!
Joint workshop Role of Machine Learning in Molecular Discovery & Scientific Understanding
Key note speaker
Prof. dr. Max Welling
Research Chair Machine Learning, University of Amsterdam
Date: 21 March 2024
Venue: Theatre Hall X, TU Delft (directions)
More information, full programme and registration form.
Join us
Whether your interest lies in developing machine learning algorithms or using them to unlock the mysteries of the molecular world, we believe this workshop will offer invaluable insights. Join us in this exploration of the future of scientific discovery, facilitated by the powerful combination of machine learning and quantum mechanics.
Programme
It promises to be a dynamic intersection of pure machine learning and statistical models applied to scientific discovery. The event will focus on exploring theoretical machine learning research and their real-world applications in molecular and material discovery.
The emphasis will be on integrating small scale experimental data and quantum mechanical calculations into machine learning frameworks, forming collaborative networks, and addressing key challenges.
Confirmed speakers
Prof. dr. Max Welling, University of Amsterdam (Key note speaker)
Title: Relating Non-Equilibrium Thermodynamics to Machine Learning: what can we learn?
- Jakub Tomczak, Eindhoven University of Technology
- Pim de Haan, University of Amsterdam / Qualcomm
- Nong Artrith, Utrecht University
- Menno Bokdam, University of Twente
- Vedran Dunjko, Leiden University
- Süleyman Er, DIFFER
- Sid Kumar, Delft University of Technology
- Will Robinson, Radboud University
- Jana Weber, Delft University of Technology
More information: Role of Machine Learning in Molecular Discovery & Scientific Understanding
Header image: Kumar et. al., 2020