Crystal-GFN: sampling crystals with desirable properties and constraints

• URL: http://arxiv.org/abs/2310.04925v2
• Date: Wed, 13 Dec 2023 16:24:44 GMT
• Title: Crystal-GFN: sampling crystals with desirable properties and constraints
• Authors: Mila AI4Science and Alex Hernandez-Garcia and Alexandre Duval and Alexandra Volokhova and Yoshua Bengio and Divya Sharma and Pierre Luc Carrier and Yasmine Benabed and Micha{\l} Koziarski and Victor Schmidt
• Abstract summary: We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, 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.
• Score: 103.79058968784163
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such 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.

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