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

• URL: http://arxiv.org/abs/2005.07609v3
• Date: Wed, 15 Dec 2021 12:05:20 GMT
• Title: An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
• Authors: Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G.Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi
• Abstract summary: We present a framework capable of general inverse design (not limited to a given set of elements or crystal structures) The framework generates new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. Results represent a significant step toward property-driven general inverse design using generative models.
• Score: 10.853822721106205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-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.

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