Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

摘要: Materials informatics has significantly accelerated the discovery and analysis of materials in past decade. One key contributors to is use on-the-fly data with high-throughput experiments, which given rise need for accurate automated estimation properties materials. In this regard, spectroscopic are widely used because these include essential information about An important requirement realisation parameters selection a similarity measure, or kernel function. The required measure should be robust terms peak shifting, broadening, noise. However, determination appropriate measures spectra from currently remain unresolved. We examined major evaluate both X-ray absorption electron energy-loss spectra. show good correspondence parameter, that is, crystal-field all measures. Pearson's correlation coefficient was highest robustness against noise broadening. obtained regression model parameter 10 Dq enabled 10 Dq, automatically estimated With regard research progress measures, methodology would make it possible extract large-scale dataset experimental data.