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. DIFFER's head of the Solar Fuels Department, Süleyman Er, will be one of the speakers. In his lecture, he will present research projects that merge AI with computational screening for identifying electrolyte molecules, which can potentially be used for energy storage in aqueous redox flow batteries.
4TU workshop on the Role of Machine Learning in Molecular Discovery & Scientific Understanding
Key note speaker: Max Welling
Research Chair Machine Learning, University of Amsterdam
09.30 - walk in with coffee
10.00 - morning session
12.00 - lunch
13.00 - afternoon sessions
17.00 - drinks & bites
Date: 21 March 2024
Venue: Theatre Hall X, TU Delft (directions)
Full programme (Pdf) - now available
Lecture Süleyman Er
Invited speaker: Süleyman Er
Title: Towards Autonomous Discovery of Energy Materials
In this talk, Er will present an overview of our research projects that merge AI with computational screening for identifying electrolyte molecules, which can potentially be used for energy storage in aqueous redox flow batteries. His approach is multi-faceted, involving:
I) Development of theoretical methodologies and AI tools to evaluate redox potentials and solubilities of candidate compounds in water , along with automated chemical space visualization and chemical price search tools for sourcing these molecules from suppliers.
II) Investigation of structure-property relationships and prioritization of compounds, aiming to pinpoint the most effective candidates for practical experiments.
III) Conducting electrochemical tests on safe and easily accessible compounds selected from our virtual library.
IV) The creation of a computational database of electroactive molecules. These examples demonstrate how AI and computational science can be coupled to provide guidance in the search for potential energy materials
Participation is free, registration is mandatory.
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