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

LLNL scientists have developed a new approach that can rapidly predict the structure and chemical composition of heterogeneous materials. In a new study in ACS Chemistry of Materials, Wonseok Jeong and Tuan Anh Pham developed a new approach that combines machine learning with X-ray absorption spectroscopy (XANES) to elucidate the chemical speciation of amorphous carbon nitrides. The research offers profound new insights into the local atomic structure of the systems, and in a broader context, represents a critical step in establishing an automated framework for rapid characterization of heterogeneous materials with intricate structures. By coupling machine learning potentials with high-fidelity atomistic simulations, the researchers establish correlations between local atomic structures and spectroscopic signatures. This correlation serves as the basis for interpreting experimental XANES data, allowing for the extraction of crucial chemical information from complex spectra. Read more at LLNL News.