Accelerating material characterization: Machine learning meets X-ray absorption spectroscopy

Lawrence Livermore National Laboratory researchers have developed a method combining machine learning with X-ray absorption spectroscopy (XANES) to rapidly predict the structure and chemical composition of heterogeneous materials. This new approach, focusing on amorphous carbon nitrides, uses machine learning to explore atomic configurations and correlate these with XANES data. The method enhances understanding of disordered materials and provides a framework for rapid material characterization, potentially applicable to various material systems and experimental techniques.

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