Portrait de Divya Sharma

Divya Sharma

Postdoctorat
Superviseur⋅e principal⋅e
Yoshua Bengio
Co-supervisor
Alex Hernández-García
Sujets de recherche
Apprentissage profond
Modèles génératifs
Modélisation moléculaire
Réseaux de neurones en graphes
Site web
GitHub

Publications

Adsorption energies are necessary but not sufficient to identify good catalysts
Shahana Chatterjee
Alexander Davis
Lena Podina
Divya Sharma
Yoshua Bengio
Alexandre AGM Duval
Oleksandr Voznyy
Alex Hern'andez-Garcia
David Rolnick
Félix Therrien
2025-12-04
ArXiv (prépublication)
arxiv.org
arxiv.org
OBELiX: a curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes
Félix Therrien
Jamal Abou Haibeh
Divya Sharma
Rhiannon Hendley
Leah Wairimu Mungai
Sun Sun
Alain Tchagang
Jiang Su
Samuel Huberman
Yoshua Bengio
Hongyu Guo
Alex Hernández-García
Homin Shin
OBELiX is a database of 599 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities … (voir plus)gathered from literature and curated by domain experts.
2025-02-19
arXiv (prépublication)
doi.org
openreview.net
Crystal-GFN: sampling materials with desirable properties and constraints
Mistal
Alex Hernández-García
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
Crystal-GFN: sampling crystals with desirable properties and constraints
Alex Hernández-García
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