2 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
DeePAW: A universal machine learning model for orbital-free ab initio calculations
Tianhao Su, Shunbo Hu, Yue Wu +5
Developing universal machine learning models for ab initio calculations is the frontier of materials cutting edge research in the new era of artificial intelligence. Here, we prese…
AI-Driven Structure Refinement of X-ray Diffraction
Bin Cao, Qian Zhang, Zhenjie Feng +4
Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak inte…
Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
Zhixun Li, Bin Cao, Rui Jiao +9
Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design…
opXRD: Open Experimental Powder X-ray Diffraction Database
Daniel Hollarek, Henrik Schopmans, Jona Östreicher +21
Powder X-ray diffraction (pXRD) experiments are a cornerstone for materials structure characterization. Despite their widespread application, analyzing pXRD diffractograms still pr…
SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification
Bin Cao, Yang Liu, Zinan Zheng +3
Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (X…