128 citations · 362 across the 31 of their papers we have counts for
33 papers
GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward
Florian Holeczek, Andreas Hinterreiter, Alex Hernandez-Garcia +2
We present GFlowState, a visual analytics system designed to illuminate the training process of Generative Flow Networks (GFlowNets or GFNs). GFlowNets are a probabilistic framewor…
The implicated scientist: on the role of AI researchers in the development of weapons systems
Alexandra Volokhova, Alex Hernandez-Garcia
Artificial intelligence (AI) technologies are increasingly used in modern weapons systems. Notably, these systems have recently been involved in mass killings and destruction at sc…
A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes
Dounia Shaaban Kabakibo, Félix Therrien, Yoshua Bengio +4
Accurate prediction of ionic conductivity is critical for the design of high-performance solid-state electrolytes in next-generation batteries. We benchmark molecular dynamics (MD)…
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim, Minsu Kim, Celine Roget +5
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in pract…
LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models
Siddharth Betala, Samuel P. Gleason, Ali Ramlaoui +12
Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented explorat…
Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee, Alexander Davis, Lena Podina +7
As a core technology for green chemical synthesis and electrochemical energy storage, electrocatalysis is central to decarbonization strategies aimed at combating climate change. I…
Irresponsible AI: big tech's influence on AI research and associated impacts
Alex Hernandez-Garcia, Alexandra Volokhova, Ezekiel Williams +2
The accelerated development, deployment and adoption of artificial intelligence systems has been fuelled by the increasing presence of big tech in the AI field. This trend has been…
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
Lena Podina, Christina Humer, Alexandre Duval +7
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind a…
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova, Léna Néhale Ezzine, Piotr Gaiński +5
Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a molecule to a target. Recently, generative…
Learning Decision Trees as Amortized Structure Inference
Mohammed Mahfoud, Ghait Boukachab, Michał Koziarski +4
Building predictive models for tabular data presents fundamental challenges, notably in scaling consistently, i.e., more resources translating to better performance, and generalizi…
OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
Félix Therrien, Jamal Abou Haibeh, Divya Sharma +10
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved…
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim, Minsu Kim, Taeyoung Yun +4
Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although reinforcement learning methods use proxy mo…
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova, Michał Koziarski, Alex Hernández-García +7
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this paper we propose to use GFlowNet for sampl…
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero, Alexandre Duval, Victor Schmidt +4
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all a…
Crystal-GFN: sampling crystals with desirable properties and constraints
Mila AI4Science, :, Alex Hernandez-Garcia +11
The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challe…
Multi-Fidelity Active Learning with GFlowNets
Alex Hernandez-Garcia, Nikita Saxena, Moksh Jain +2
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to…
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang, Alex Hernandez-Garcia, Paula Harder +5
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate…
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia +4
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural netwo…
Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning
Alexandra Sasha Luccioni, Alex Hernandez-Garcia
Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of…
GFlowNets for AI-Driven Scientific Discovery
Moksh Jain, Tristan Deleu, Jason Hartford +3
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 scien…
A theory of continuous generative flow networks
Salem Lahlou, Tristan Deleu, Pablo Lemos +6
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A…
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Alexandre Duval, Victor Schmidt, Santiago Miret +3
Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in numerous…
Multi-Objective GFlowNets
Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia +4
We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learning such as drug discovery and material d…
Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning
Siba Moussa, Michael Kilgour, Clara Jans +4
Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for exam…