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
Holographic Neural Network Achieves Multi-Task AI via Light-Based Computing
Researchers have developed a holographic neural network using optics to perform multiple AI classification tasks simultaneously without hardware reconfiguration.
•EurekAlert
Rethinking Multimodal Cardiovascular AI: When Models Should Defer Predictions
A commentary in Medicine Discovery argues cardiovascular AI models must learn to defer predictions when key modalities are missing, as missingness and asynchrony are clinical realities.
•EurekAlert
MSKAI Unveils AI Tool for Embryo Cell Analysis and Imaging-Driven Cancer Insights
MSK researchers employ AI and advanced imaging to reveal cancer mechanisms and track embryonic development at the single-cell level.