Alex Hernández-García

Biography

Alex Hernandez-Garcia is an assistant professor at the Université de Montréal, a core academic member at Mila, IVADO professor and member of the Institut Courtois. His machine learning research is motivated by scientific applications to tackle the climate crisis and other societal challenges. In particular, a current focus of his work is active and generative machine learning to facilitate scientific discoveries, such as new materials and antibiotics. He also advocates for a critical examination of the impacts of artificial intelligence, is a strong proponent of open science and is active in initiatives about making science more inclusive, equitable, open, reproducible, transparent and environmentally conscious.

Current Students

Tim Arni

Research Intern - Université de Montréal

Dionessa Biton

Master's Research - Université de Montréal

Hyeonah Kim

Postdoctorate - Université de Montréal

Co-supervisor :

Pierre-Louis Lemaire

Collaborating researcher - Polytechnique Montréal Montréal

Co-supervisor :

Ameer Nizami

PhD - Concordia University

Ola Onabola

PhD - Université de Montréal

Master's Research - Université de Montréal

Luca Scimeca

Collaborating Alumni - Université de Montréal

Dounia Shaaban Kabakibo

PhD - Université de Montréal

Principal supervisor :

Postdoctorate

Principal supervisor :

Publications

PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design

Alexandre Duval

Victor Schmidt

Santiago Miret

Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electro… (see more)chemical reactions involved in numerous industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis. To reduce the energy spent on such activities, we must quickly discover more efficient catalysts to drive electrochemical reactions. Machine learning (ML) holds the potential to efficiently model materials properties from large amounts of data, accelerating electrocatalyst design. The Open Catalyst Project OC20 dataset was constructed to that end. However, ML models trained on OC20 are still neither scalable nor accurate enough for practical applications. In this paper, we propose task-specific innovations applicable to most architectures, enhancing both computational efficiency and accuracy. This includes improvements in (1) the graph creation step, (2) atom representations, (3) the energy prediction head, and (4) the force prediction head. We describe these contributions, referred to as PhAST, and evaluate them thoroughly on multiple architectures. Overall, PhAST improves energy MAE by 4 to 42

2023-12-31

Journal of Machine Learning Research (published)

Crystal-GFN: sampling materials with desirable properties and constraints

Mistal

Alexandre AGM Duval

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre Duval

Victor Schmidt

Santiago Miret

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (see more)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Cheng-Hao Liu

Santiago Miret

Pablo Lemos

Luca Thiede

Zichao Yan

Alán Aspuru-Guzik

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (see more)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

2023-10-26

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

Crystal-GFN: sampling crystals with desirable properties and constraints

Alexandre AGM Duval

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (see more)as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.

2023-10-06

ArXiv (preprint)

Multi-variable Hard Physical Constraints for Climate Model Downscaling

Jose Gonz'alez-Abad

'Alex Hern'andez-Garc'ia

Paula Harder

Jos'e Manuel Guti'errez

2023-08-01

ArXiv (preprint)

Multi-Objective GFlowNets

Moksh Jain

Sharath Chandra Raparthy

Jarrid Rector-Brooks

Santiago Miret

Emmanuel Bengio

We study the problem of generating diverse candidates in the context of Multi-Objective Optimization. In many applications of machine learni… (see more)ng such as drug discovery and material design, the goal is to generate candidates which simultaneously optimize a set of potentially conflicting objectives. Moreover, these objectives are often imperfect evaluations of some underlying property of interest, making it important to generate diverse candidates to have multiple options for expensive downstream evaluations. We propose Multi-Objective GFlowNets (MOGFNs), a novel method for generating diverse Pareto optimal solutions, based on GFlowNets. We introduce two variants of MOGFNs: MOGFN-PC, which models a family of independent sub-problems defined by a scalarization function, with reward-conditional GFlowNets, and MOGFN-AL, which solves a sequence of sub-problems defined by an acquisition function in an active learning loop. Our experiments on wide variety of synthetic and benchmark tasks demonstrate advantages of the proposed methods in terms of the Pareto performance and importantly, improved candidate diversity, which is the main contribution of this work.

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (published)

proceedings.mlr.press

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang

Paula Harder

Venkatesh Ramesh

Prasanna Sattegeri

D. Szwarcman

Campbell Watson

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (see more)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.

2023-05-22

ArXiv (preprint)

GFlowNets for AI-Driven Scientific Discovery

Moksh Jain

Tristan Deleu

Jason Hartford

Cheng-Hao Liu

Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (see more)ace 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.

2022-12-31

Digital Discovery (published)

Hard-Constrained Deep Learning for Climate Downscaling

Paula Harder

Venkatesh Ramesh

Qidong Yang

Prasanna Sattegeri

D. Szwarcman

Campbell Watson

The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (see more)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather data sets. Besides enabling faster and more accurate climate predictions through downscaling, we also show that our novel methodologies can improve super-resolution for satellite data and natural images data sets.

2022-12-31

Journal of Machine Learning Research (published)

A Theory of Continuous Generative Flow Networks

Lena Nehale Ezzine

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (see more)ributions over compositional objects. A key limitation of GFlowNets until this time has been that they are restricted to discrete spaces. We present a theory for generalized GFlowNets, which encompasses both existing discrete GFlowNets and ones with continuous or hybrid state spaces, and perform experiments with two goals in mind. First, we illustrate critical points of the theory and the importance of various assumptions. Second, we empirically demonstrate how observations about discrete GFlowNets transfer to the continuous case and show strong results compared to non-GFlowNet baselines on several previously studied tasks. This work greatly widens the perspectives for the application of GFlowNets in probabilistic inference and various modeling settings.

2022-12-31

ICML (published)

proceedings.mlr.press

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning

Siba Moussa

Michael Kilgour

Clara Jans

Miroslava Cuperlovic‐culf