An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

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Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, stateof-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis.
Publisher
ELSEVIER
Issue Date
2022-01
Language
English
Article Type
Article
Citation

MATTER, v.5, no.1, pp.314 - 335

ISSN
2590-2393
DOI
10.1016/j.matt.2021.11.032
URI
http://hdl.handle.net/10203/292010
Appears in Collection
CBE-Journal Papers(저널논문)
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