The National Institute of Standards and Technology (NIST) is developing advanced quantitative analysis methods that combine machine learning with physical models to improve the interpretation of complex materials data. The goal is to automate the analysis process and reduce the time required from weeks to days, while also making the analysis more repeatable and less dependent on human expertise.
The current focus is on x-ray analysis techniques such as diffraction and EXAFS, which produce data in reciprocal space that needs to be analyzed to extract physical properties like lattice parameters and grain size. NIST researchers are exploring new methods for analyzing x-ray data and integrating machine learning algorithms with physical models to create more accurate and efficient analysis tools.
The development of these advanced quantitative analysis methods has the potential to significantly impact the field of materials science by enabling faster and more reliable characterization of advanced materials, which could accelerate the development of new technologies and materials.
Keywords: lattice parameters, machine learning, physical models, reciprocal-space, x-ray analysis
