Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.matt.2021.11.032
Title: | An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties | Authors: | REN ZEKUN Tian, Siyu Isaac Parker Noh, Juhwan Oviedo, Felipe Xing, Guangzong JIALI LI Liang, Qiaohao Zhu, Ruiming Armin Gerhard Aberle Sun, Shijing WANG XIAONAN Liu, Yi Li, Qianxiao Jayavelu, Senthilnath Hippalgaonkar, Kedar Jung, Yousung Buonassisi, Tonio |
Keywords: | general inverse design solid-state materials invertible crystallographic representation generalized crystallographic representation property-structured latent space variational autoencoder machine learning thermoelectrics generative model |
Issue Date: | 20-Dec-2021 | Publisher: | Cell Press | Citation: | REN ZEKUN, Tian, Siyu Isaac Parker, Noh, Juhwan, Oviedo, Felipe, Xing, Guangzong, JIALI LI, Liang, Qiaohao, Zhu, Ruiming, Armin Gerhard Aberle, Sun, Shijing, WANG XIAONAN, Liu, Yi, Li, Qianxiao, Jayavelu, Senthilnath, Hippalgaonkar, Kedar, Jung, Yousung, Buonassisi, Tonio (2021-12-20). An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties. Matter. ScholarBank@NUS Repository. https://doi.org/10.1016/j.matt.2021.11.032 | 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. | Source Title: | Matter | URI: | https://scholarbank.nus.edu.sg/handle/10635/211596 | ISSN: | 2590-2385 | DOI: | 10.1016/j.matt.2021.11.032 |
Appears in Collections: | Staff Publications Elements |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
Accepted Manuscript.pdf | Accepted version | 1.47 MB | Adobe PDF | OPEN | Post-print | View/Download |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.