Generative modelling and active learning for scientific discoveries

16^th December 2025, 13:00 Ashton Lecture Theatre
Alex Hernadez-Garcia
Université de Montréal

Abstract

Advancing scientific discoveries can play a fundamental role in tackling some of the most pressing challenges for humanity, such as the climate crisis, the threat of pandemics and antibiotic resistance. Meanwhile, the increasing capacity to generate large amounts of data, the progress in computer engineering and the maturity of machine learning methods offer an excellent opportunity to assist scientific progress. In this seminar, I would like to offer an overview of our recent work on generative modelling and active learning. In particular, the focus will be on the potential of generative flow networks (GFlowNets) as a generative model for scientific discoveries. I will offer an introduction to GFlowNets and explain how we have adapted this method to incorporate domain knowledge from crystallography, physics and chemistry in the form of hard constraints, to efficiently discover new materials with desirable properties. I will also present our recent algorithm for multi-fidelity active learning with GFlowNets, designed to efficiently explore combinatorially large, high-dimensional and mixed spaces (discrete and continuous), inspired by challenges in materials and drug discovery.