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

Abstract

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.npj Computational Materials (2018) 4:12 ; doi:10.1038/s41524-018-0067-x INTRODUCTION X-ray absorption spectroscopy (XAS) is a widely used technique in the study of the properties, physical states, and local environments of materials.1-3 When incident X-ray photons with energy greater than the binding energy are absorbed by an atom, a corelevel electron is removed from its quantum level. In XAS, the absorption coefficient, μ(E) is measured as a function of X-ray energy E. Detailed descriptions of X-ray absorption theory and equation have been included in many excellent books and review papers.

A Critical Review of Machine Learning of Energy Materials

“…These models are generally more accurate and have low model variances. The ensemble methods have been applied in the construction of accurate ML models for predicting atomic local environment from K‐edge X‐ray near‐edge structure (XANES) . or in various tree‐based ensemble methods …”

“…Finally, an application of ML models that has received far less attention in other reviews is their potential to enhance experimental characterization. The interpretation and labeling of experimental images (e.g., scanning transmission electron microscopy (STEM)) and spectra (e.g., X‐ray diffraction (XRD), X‐ray absorption near‐edge structure (XANES), nuclear magnetic resonance (NMR), etc.) are today still mostly painstakingly carried out by humans.…”

Harnessing the Materials Project for machine-learning and accelerated discovery

“…Simultaneously introduced with XASdb, a database containing ∼800,000 K-edge XANES spectra, a novel ensemble-learned spectrum identification algorithm was proposed to enable instant matching between experimental XAS spectra and the references in XASdb. 48 The algorithm classifies an input spectrum according to chemistry and oxidation state, using a group of 33 weakly correlated algorithms in an ensemble akin to a random forest, to span the whole space of materials chemistry and oxidation states available in the Materials Project. This divide-and-conquer approach correctly identified the oxidation state and coordination environment with 84.2% accuracy in a test of 19 highquality experimental spectra and is currently implemented in the Materials Project for rapid classification of measured XAS spectra.…”

Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network

“…Convolutional neural networks (CNN) have been applied in a wide variety of fields, such as image recognition (Hu et al, 2018), computer vision (Kendall & Yarin, 2017), medical image analysis (Poplin et al, 2018), and material inspections (Park et al, 2016). They have also been used for the analysis of X-ray experiments, such as GISAXS (Liu et al, 2019) and X-ray absorption near-edge structure (Timoshenko et al, 2017;Zheng et al, 2018). The open-source TensorFlow machinelearning library (Abadi et al, 2015), which is adaptable to the field of deep learning (Goodfellow et al, 2016), is available for free.…”