Alex Hernandez-Garcia

Divya Sharma

pierre luc carrier

Michał Koziarski

Victor Schmidt

2023-10-27

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

Multi-Fidelity Active Learning with GFlowNets

Alex Hernandez-Garcia

Nikita Saxena

Moksh J. Jain

Cheng-Hao Liu

In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanw… (voir plus)hile, the progress in machine learning has turned it into a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, high-dimensional spaces, where querying a high fidelity, black-box objective function is very expensive. Progress in machine learning methods that can efficiently tackle such problems would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose the use of GFlowNets for multi-fidelity active learning, where multiple approximations of the black-box function are available at lower fidelity and cost. GFlowNets are recently proposed methods for amortised probabilistic inference that have proven efficient for exploring large, high-dimensional spaces and can hence be practical in the multi-fidelity setting too. Here, we describe our algorithm for multi-fidelity active learning with GFlowNets and evaluate its performance in both well-studied synthetic tasks and practically relevant applications of molecular discovery. Our results show that multi-fidelity active learning with GFlowNets can efficiently leverage the availability of multiple oracles with different costs and fidelities to accelerate scientific discovery and engineering design.

2023-10-27

NeurIPS.cc/2023/Workshop/ReALML (published)

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

Alvaro Carbonero

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (voir plus)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.

2023-10-27

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

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Alex Hernandez-Garcia

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… (voir plus)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.

2023-10-27

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

Crystal-GFN: sampling crystals with desirable properties and constraints

Alex Hernandez-Garcia

Alexandre AGM Duval

Alexandra Volokhova

Divya Sharma

pierre luc carrier

Michał Koziarski

Victor Schmidt

Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such … (voir plus)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.

2023-10-07

ArXiv (prépublication)

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

2023-08-02

ArXiv (prépublication)

FAENet: Frame Averaging Equivariant GNN for Materials Modeling

Alexandre AGM Duval

Victor Schmidt

Alex Hernandez-Garcia

Santiago Miret

Fragkiskos D. Malliaros

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.

2023-07-03

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

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang

Alex Hernandez-Garcia

Paula Harder

Venkatesh Ramesh

Prasanna Sattegeri

D. Szwarcman

C. Watson

Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)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.

2023-05-23

ArXiv (prépublication)

Multi-Objective GFlowNets

Moksh J. Jain

Sharath Chandra Raparthy

Alex Hernandez-Garcia

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… (voir plus)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.

2023-04-24

ICML.cc/2023/Conference (poster)

GFlowNets for AI-Driven Scientific Discovery

Moksh J. Jain

Tristan Deleu

Jason Hartford

Cheng-Hao Liu

Alex Hernandez-Garcia

Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (voir plus)ace of scientific discovery. While science has traditionally relied...

2023-01-01

Digital Discovery (publié)

A theory of continuous generative flow networks

Salem Lahlou

Tristan Deleu

Pablo Lemos

Dinghuai Zhang

Alexandra Volokhova

Alex Hernandez-Garcia

Lena Nehale Ezzine

Nikolay Malkin

2023-01-01

ICML (publié)

A theory of continuous generative flow networks

Salem Lahlou

Tristan Deleu

Pablo Lemos

Dinghuai Zhang

Alexandra Volokhova

Alex Hernandez-Garcia

Lena Nehale Ezzine

Nikolay Malkin

Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target dist… (voir plus)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.

2023-01-01

ICML (publié)