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.
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.
The paper presents a cross‑modal learning framework that predicts three‑dimensional dislocation density fields directly from X‑ray diffraction data. By pairing simulated dislocation density fields with virtual diffraction patterns and embedding them into a shared 2‑D latent space via contrastive learning, the authors achieve strong alignment between structural and diffraction representations. Experiments show that model performance improves rapidly with dataset size, reaching near‑saturation with about 500 representative observations out of 10,000, and the predicted structures capture the dominant spatial features of the underlying microstructures.
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.
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: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:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.
arXiv:2609.00059v1 Announce Type: cross Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled...
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
arXiv:2609.14348v1 Announce Type: cross Abstract: Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations wit...
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.
arXiv:2502. 02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.
4DMulti is a physics‑guided learning framework that automates multicomponent identification from large‑scale four‑dimensional scanning transmission electron microscopy data. It leverages a 6‑million‑pattern diffraction database, a retrieval‑conditioned latent diffusion transformer (Sim2real) for realistic pattern generation, and a rotation‑invariant convolutional network for phase classification, achieving 98.82% accuracy on a five‑phase benchmark. The method introduces a diffraction‑inferred structural complexity metric and produces single‑nanometer‑resolution structural maps of complex material interfaces such as superconducting heterostructures, corroded alloys, and degraded solid‑state battery interfaces.