Researchers have developed an AI-based approach to automate and enhance the analysis of X-ray absorption spectroscopy (XAS) data for materials science.
Key Details
- 1X-ray absorption spectroscopy (XAS) is critical for determining materials’ properties but is traditionally labor-intensive and requires expertise.
- 2A Tokyo University of Science team applied machine learning, especially UMAP, to automate analysis and classification of XAS data.
- 3UMAP outperformed other dimensionality reduction techniques in accurately identifying boron nitride structures and defects.
- 4The AI-based system proved robust against real-world noise in experimental XAS data and offers higher accuracy than prior statistical approaches.
- 5The method is being deployed at the Nano-Terasu synchrotron radiation center and is expected to accelerate material design.
Why It Matters
Automating XAS analysis with AI can dramatically speed up and objectify material characterization and design, potentially influencing imaging physics, research, and advanced imaging informatics. This approach demonstrates a broader application of AI to complex imaging data, which could cross-inform developments in radiology AI.
Source
EurekAlert
Related News
•EurekAlert
AI-Powered OCT Enables Rapid 'Optical Biopsy' for Early Endometrial Cancer Detection
A team at Washington University has developed a catheter-based 3D OCT system with AI to quickly and noninvasively detect early endometrial cancers.
•EurekAlert
AI Clinical Reasoning in Diagnostics and Digital Fatigue in Healthcare
Recent JMIR features explore large language models in clinical diagnostics and digital fatigue among healthcare professionals.
•EurekAlert
KAIST, MIT, Microsoft Develop Efficient AI Image Upsampling for Robotics
KAIST, MIT, and Microsoft have created 'Upsample Anything,' a training-free AI method to restore high-resolution visual data from compressed images with up to 16x improved GPU memory efficiency.