arXiv AI
PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Jun 15
XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models
arXiv:2606. 14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science.
arXiv AI
Oct 7
XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction
XDecomposer is a prior‑free framework that jointly decomposes and identifies multiphase X‑ray diffraction patterns without requiring candidate phase lists, structural templates, or knowledge of the number of phases. It treats multiphase analysis as a set prediction problem, inferring an unordered set of phase‑resolved components, their mixture proportions, and structural representations within a single architecture. Experiments on simulated and experimental data demonstrate improved reconstruction accuracy and phase identification across diverse chemical systems, with strong generalization to unseen mixtures.
arXiv AI
Aug 10
ED-CSP: Crystal Structure Prediction from Electron Diffraction
arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.
arXiv Machine Learning
Oct 9
PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
PXtal is a new framework that learns aligned representations for powder X‑ray diffraction (PXRD) patterns and crystal structures, addressing the information asymmetry inherent in compressing 3D crystal data into 1D diffraction patterns. It employs Unbalanced Optimal Transport to adjust cross‑modal coupling and uses a coupling‑level generalized Kullback‑Leibler divergence to supervise the transport plan. Across six test sets—including four zero‑shot transfer scenarios—PXtal consistently outperforms baseline models in retrieving crystal candidates from PXRD data, especially when similar diffraction patterns correspond to distinct crystals, and its encoders transfer effectively to downstream materials science tasks.
arXiv AI
Oct 27
Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
arXiv:2610.33310v1 Announce Type: cross Abstract: Powder X-ray diffraction (XRD) is the primary high-throughput method for resolving crystal structures, a cornerstone of materials science. As high-th...
arXiv Machine Learning
Jul 10
MatBind: A Shared Embedding Space for Multimodal Materials Characterization
arXiv:2607. 08470v1 Announce Type: new Abstract: Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural language -- each of which captures a different facet of the same physical object.