Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials

Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials

Abstract Self‐supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and...

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Bibliographic Details
Main Authors: Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Rongzhi Dong, Nihang Fu, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu
Format: Article
Language:English
Published: Wiley 2024-09-01
Series:Advanced Science
Subjects:
blank filling
deep learning
language models
materials discovery
materials generator
Online Access:https://doi.org/10.1002/advs.202304305
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https://doi.org/10.1002/advs.202304305

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