GFlowNets for AI-Driven Scientific Discovery

by   Moksh Jain, et al.

Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While science has traditionally relied on trial and error and even serendipity to a large extent, the last few decades have seen a surge of data-driven scientific discoveries. However, in order to truly leverage large-scale data sets and high-throughput experimental setups, machine learning methods will need to be further improved and better integrated in the scientific discovery pipeline. A key challenge for current machine learning methods in this context is the efficient exploration of very large search spaces, which requires techniques for estimating reducible (epistemic) uncertainty and generating sets of diverse and informative experiments to perform. This motivated a new probabilistic machine learning framework called GFlowNets, which can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. GFlowNets learn to sample from a distribution given indirectly by a reward function corresponding to an unnormalized probability, which enables sampling diverse, high-reward candidates. GFlowNets can also be used to form efficient and amortized Bayesian posterior estimators for causal models conditioned on the already acquired experimental data. Having such posterior models can then provide estimators of epistemic uncertainty and information gain that can drive an experimental design policy. Altogether, here we will argue that GFlowNets can become a valuable tool for AI-driven scientific discovery, especially in scenarios of very large candidate spaces where we have access to cheap but inaccurate measurements or to expensive but accurate measurements. This is a common setting in the context of drug and material discovery, which we use as examples throughout the paper.


Deep Learning Opacity in Scientific Discovery

Philosophers have recently focused on critical, epistemological challeng...

Multi-Fidelity Active Learning with GFlowNets

In the last decades, the capacity to generate large amounts of data in s...

Toward Democratizing Access to Facilities Data: A Framework for Intelligent Data Discovery and Delivery

Data collected by large-scale instruments, observatories, and sensor net...

GT4SD: Generative Toolkit for Scientific Discovery

With the growing availability of data within various scientific domains,...

FED-CD: Federated Causal Discovery from Interventional and Observational Data

Causal discovery, the inference of causal relations from data, is a core...

Up to two billion times acceleration of scientific simulations with deep neural architecture search

Computer simulations are invaluable tools for scientific discovery. Howe...

Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope

We leverage state-of-the-art machine learning methods and a decade's wor...

Please sign up or login with your details

Forgot password? Click here to reset