An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning

Hang XIAO, Rong LI, Xiaoyang SHI, Yan CHEN*, Liangliang ZHU*, Xi CHEN*, Lei WANG*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

The past decade has witnessed rapid progress in deep learning for molecular design, owing to the availability of invertible and invariant representations for molecules such as simplified molecular-input line-entry system (SMILES), which has powered cheminformatics since the late 1980s. However, the design of elemental components and their structural arrangement in solid-state materials to achieve certain desired properties is still a long-standing challenge in physics, chemistry and biology. This is primarily due to, unlike molecular inverse design, the lack of an invertible crystal representation that satisfies translational, rotational, and permutational invariances. To address this issue, we have developed a simplified line-input crystal-encoding system (SLICES), which is a string-based crystal representation that satisfies both invertibility and invariances. The reconstruction routine of SLICES successfully reconstructed 94.95% of over 40,000 structurally and chemically diverse crystal structures, showcasing an unprecedented invertibility. Furthermore, by only encoding compositional and topological data, SLICES guarantees invariances. We demonstrate the application of SLICES in the inverse design of direct narrow-gap semiconductors for optoelectronic applications. As a string-based, invertible, and invariant crystal representation, SLICES shows promise as a useful tool for in silico materials discovery.
Original languageEnglish
Article number7027
Number of pages12
JournalNature Communications
Volume14
Issue number1
DOIs
Publication statusE-pub ahead of print - 2 Nov 2023

Bibliographical note

Many useful discussions with Prof. Hisashi Naito from Nagoya University are acknowledged.

Publisher Copyright:
© 2023, The Author(s).

Fingerprint

Dive into the research topics of 'An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning'. Together they form a unique fingerprint.

Cite this