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Creators/Authors contains: "Ling, Hongyi"

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  1. ; ; ; ; ; ; ; ; ; ; et al (, ArXiv)
    We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods. 
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    Free, publicly-accessible full text available December 15, 2025
  2. ; ; ; ; ; ; ; ; ; ; et al (, 38th Conference on Neural Information Processing Systems (NeurIPS 2024).)
    Globerson, A; Mackey, L; Belgrave, D; Fan, A; Paquet, U; Tomczak, J; Zhang, C (Ed.)
    We consider the problem of crystal materials generation using language models (LMs). A key step is to convert 3D crystal structures into 1D sequences to be processed by LMs. Prior studies used the crystallographic information framework (CIF) file stream, which fails to ensure SE(3) and periodic invariance and may not lead to unique sequence representations for a given crystal structure. Here, we propose a novel method, known as Mat2Seq, to tackle this challenge. Mat2Seq converts 3D crystal structures into 1D sequences and ensures that different mathematical descriptions of the same crystal are represented in a single unique sequence, thereby provably achieving SE(3) and periodic invariance. Experimental results show that, with language models, Mat2Seq achieves promising performance in crystal structure generation as compared with prior methods. 
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    Free, publicly-accessible full text available December 1, 2025
  3. ; ; ; ; ; ; ; ; ; (, NeurIPS Foundation/OpenReview)
    Free, publicly-accessible full text available November 6, 2025
  4. ; ; ; ; (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ; ; ; ; et al (, Foundations and Trends® in Machine Learning)
    Free, publicly-accessible full text available January 1, 2026