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

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

SUBMITTER: Xiao H 

PROVIDER: S-EPMC10622439 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature