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Pierre-Paul De Breuck

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Crystal-GFN: sampling crystals with desirable properties and constraints
Mila AI4Science
Alex Hernández-García
Alexandre Duval
Alexandra Volokhova
Yoshua Bengio
Divya Sharma
Pierre Luc Carrier
Yasmine Benabed
Michał Koziarski
Victor Schmidt
Gian-Marco Rignanese
Pierre-Paul De Breuck
Paulette Clancy
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