Automated generation and ensemble-learned matching of X-ray absorption spectra

X-ray absorption spectroscopy(XAS)is a widely used materials characterization technique to determine oxidation states,coordination environment,and other local atomic structure information.Analysis of XAS relies on comparison of measured spectra to reliable reference spectra.However,existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage.In this work,we report the development of XASdb,a large database of computed reference XAS,and an Ensemble-Learned Spectra IdEntification(ELSIE)algorithm for the matching of spectra.XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra(XANES)for over 40,000 materials from the open-science Materials Project database.We discuss a high-throughput automation framework for FEFF calculations,built on robust,rigorously benchmarked parameters.FEFF is a computer program uses a real-space Green’s function approach to calculate X-ray absorption spectra.We will demonstrate that the ELSIE algorithm,which combines 33 weak“learners”comprising a set of preprocessing steps and a similarity metric,can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures.The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project,providing an important new public resource for the analysis of XAS to all materials researchers.Finally,the ELSIE algorithm itself has been made available as part of veidt,an open source machine-learning library for materials science.

Evaluation of thermodynamic equations of state across chemistry and structure in the materials project

Thermodynamic equations of state(EOS)for crystalline solids describe material behaviors under changes in pressure,volume,entropy and temperature,making them fundamental to scientific research in a wide range of fields including geophysics,energy storage and development of novel materials.Despite over a century of theoretical development and experimental testing of energy–volume(E–V)EOS for solids,there is still a lack of consensus with regard to which equation is indeed optimal,as well as to what metric is most appropriate for making this judgment.In this study,several metrics were used to evaluate quality of fit for 8 different EOS across 87 elements and over 100 compounds which appear in the literature.Our findings do not indicate a clear“best”EOS,but we identify three which consistently perform well relative to the rest of the set.Furthermore,we find that for the aggregate data set,the RMSrD is not strongly correlated with the nature of the compound,e.g.,whether it is a metal,insulator,or semiconductor,nor the bulk modulus for any of the EOS,indicating that a single equation can be used across a broad range of classes of materials.

Author Correction:Automated generation and ensemblelearned matching of X-ray absorption spectra

Correction to:npj Computational Materials https://doi.org/10.1038/s41524-018-0067-x,published online 20 March 2018 The following text has been added to the Acknowledgements section:“L.F.J.P.acknowledges support from the National Science Foundation(DMREF-1627583).”