Portrait de Alexandra Volokhova

Alexandra Volokhova

Représentant du laboratoire
Doctorat
Sujets de recherche
Apprentissage profond
Modèles génératifs
Modèles probabilistes
Modélisation moléculaire

Biographie

Sasha est étudiante en cinquième année de doctorat au sein du groupe du professeur Yoshua Bengio. Elle est passionnée par ses recherches sur l'IA au service de la découverte scientifique ainsi que par les approches critiques de la recherche et des technologies en IA. Elle a contribué à la communauté du Mila de diverses manières tout au long de son parcours doctoral et espère continuer à rassembler les gens pour favoriser la co-création et la co-libération collectives. N'hésitez pas à la contacter si vous souhaitez discuter !

Publications

Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova
Lena Nehale Ezzine
Piotr Gainski
Luca Scimeca
Emmanuel Bengio
Prudencio Tossou
Yoshua Bengio
Alex Hernandez-Garcia
2025-06-10
ICML.cc/2025/Workshop/GenBio (poster)
doi.org
openreview.net
Crystal-GFN: sampling materials with desirable properties and constraints
Mistal
Alex Hernandez-Garcia
Alexandra Volokhova
Alexandre AGM Duval
Yoshua Bengio
Divya Sharma
Pierre Luc Carrier
Michał Koziarski
Victor Schmidt
2023-10-26
NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)
openreview.net
openreview.net
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
Yoshua Bengio
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.
2023-10-26
NeurIPS.cc/2023/Workshop/AI4Mat (poster)
doi.org
openreview.net
Crystal-GFN: sampling crystals with desirable properties and constraints
Alex Hernandez-Garcia
Alexandre AGM Duval
Alexandra Volokhova
Yoshua Bengio
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.
2023-10-06
ArXiv (prépublication)
doi.org
arxiv.org
A Theory of Continuous Generative Flow Networks
Salem Lahlou
Tristan Deleu
Pablo Lemos
Dinghuai Zhang
Alexandra Volokhova
Alex Hernandez-Garcia
Lena Nehale Ezzine
Yoshua Bengio
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.
2022-12-31
ICML (publié)
doi.org
proceedings.mlr.press
Generative Flow Networks for Discrete Probabilistic Modeling
Dinghuai Zhang
Nikolay Malkin
Zhen Liu
Alexandra Volokhova
Aaron Courville
Yoshua Bengio
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Buil… (voir plus)ding upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic data construction policy and thus amortize expensive MCMC exploration into a fixed number of actions sampled from a GFlowNet. We show how GFlowNets can approximately perform large-block Gibbs sampling to mix between modes. We propose a framework to jointly train a GFlowNet with an energy function, so that the GFlowNet learns to sample from the energy distribution, while the energy learns with an approximate MLE objective with negative samples from the GFlowNet. We demonstrate EB-GFN's effectiveness on various probabilistic modeling tasks. Code is publicly available at https://github.com/zdhNarsil/EB_GFN.
2022-06-27
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press