用 JEPA 自监督学习粉末 X 射线衍射图谱表征
Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
AI 导读
研究者采用自监督联合嵌入预测架构（JEPA）从一维粉末 X 射线衍射（XRD）图谱中学习表征嵌入，通过在潜空间内优化掩码自预测损失捕获晶体学语义。在冻结的 JEPA 表征上训练的轻量 attentive probe，在空间群分类和晶格参数估计上接近大得多的监督视觉 Transformer。潜空间解码还能在单相晶体表征之间生成逼真的多相混合物图谱，这一连续结构无需多相监督即涌现。
正文 · 原文
Abstract:Powder X-ray diffraction (XRD) is the primary high-throughput method for resolving crystal structures, a cornerstone of materials science. As high-throughput synthesis and self-driving laboratories advance, automated XRD pattern analysis has become vital for accelerating materials discovery. However, traditional matrix factorization methods fail when lattice variations cause non-linear peak shifts. At the same time, supervised learning approaches face a combinatorial explosion when handling multi-phase mixtures. To overcome these challenges, we adopt a self-supervised Joint Embedding Predictive Architecture (JEPA) to learn representation embeddings from 1D diffraction patterns. By optimizing a masked self-prediction loss directly within the latent space, the framework captures high-level crystallographic semantics. Downstream evaluations show that lightweight attentive probes trained on frozen JEPA representations achieve space group classification and lattice parameter estimation accuracy close to much larger supervised vision transformers. Furthermore, latent probing reveals that the learned representation space naturally organizes physical transformations, isolating smooth trajectories for peak broadening. Interestingly, decoding along latent paths between single-phase crystal representations yields realistic multi-phase mixture patterns, demonstrating an emergent continuous structure learned without explicit multi-phase supervision.
| Comments: | 8 pages, 6 figures, 1 table. Accepted by AAAI 2026 Fall Symposium Series Workshop "Parsimonious Scientific Machine Learning: Accelerating Scientific Discovery with AI under Data and Compute Constraints" |
| Subjects: | Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.33310 [cond-mat.mtrl-sci] |
| (or arXiv:2610.33310v1 [cond-mat.mtrl-sci] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33310 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ming-Chiang Chang [view email]
[v1]
Fri, 23 Oct 2026 21:24:33 UTC (3,145 KB)
来源：arXiv cs.AI · arxiv.org