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Divya Sharma

divya.sharma@mila.quebec
Collaborating Alumni
Supervisor
Yoshua Bengio
Website
Github

Publications

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
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (spotlight)
openreview.net
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 … (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.
2023-10-07
ArXiv (preprint)
doi.org
arxiv.org