arXiv AI
Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
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.
Hugging Face Trending Papers
Sep 15
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction a...
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.
Hugging Face Trending Papers
Oct 6
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downstream tasks. While deep learni...
arXiv AI
Oct 7
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to ge...
arXiv Machine Learning
Sep 17
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
The thesis presents AI frameworks that accelerate crystalline materials discovery by tackling both crystal property prediction and crystal structure generation. It introduces CrysXPP, CrysGNN, and CrysMMNet for efficient, data‑sparse property prediction using graph autoencoding, self‑supervised pretraining, and multimodal learning. For generation, TGDMat is a text‑guided diffusion model that jointly learns lattice parameters, atomic types, and coordinates, enabling valid, stable, and conditionally generated periodic materials.