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      Classification of Local Chemical Environments from X-ray Absorption Spectra using Supervised Machine Learning

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          Abstract

          X-ray absorption spectroscopy is a premier, element-specific technique for materials characterization. Specifically, the x-ray absorption near edge structure (XANES) encodes important information about the local chemical environment of an absorbing atom, including coordination number, symmetry and oxidation state. Interpreting XANES spectra is a key step towards understanding the structural and electronic properties of materials, and as such, extracting structural and electronic descriptors from XANES spectra is akin to solving a challenging inverse problem. Existing methods rely on empirical fingerprints, which are often qualitative or semi-quantitative and not transferable. In this study, we present a machine learning-based approach, which is capable of classifying the local coordination environments of the absorbing atom from simulated K-edge XANES spectra. The machine learning classifiers can learn important spectral features in a broad energy range without human bias, and once trained, can make predictions on the fly. The robustness and fidelity of the machine learning method are demonstrated by an average 86% accuracy across the wide chemical space of oxides in eight 3d transition metal families. We found that spectral features beyond the pre-edge region play an important role in the local structure classification problem, especially for the late 3d transition metal elements.

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          Battery materials for ultrafast charging and discharging.

          The storage of electrical energy at high charge and discharge rate is an important technology in today's society, and can enable hybrid and plug-in hybrid electric vehicles and provide back-up for wind and solar energy. It is typically believed that in electrochemical systems very high power rates can only be achieved with supercapacitors, which trade high power for low energy density as they only store energy by surface adsorption reactions of charged species on an electrode material. Here we show that batteries which obtain high energy density by storing charge in the bulk of a material can also achieve ultrahigh discharge rates, comparable to those of supercapacitors. We realize this in LiFePO(4) (ref. 6), a material with high lithium bulk mobility, by creating a fast ion-conducting surface phase through controlled off-stoichiometry. A rate capability equivalent to full battery discharge in 10-20 s can be achieved.
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            Learning phase transitions by confusion

            A neural-network technique can exploit the power of machine learning to mine the exponentially large data sets characterizing the state space of condensed-matter systems. Topological transitions and many-body localization are first on the list.
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              Machine learning in catalysis

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                Author and article information

                Journal
                03 January 2019
                Article
                1901.00788

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                11 numbered pages and 6 figures
                cond-mat.mtrl-sci

                Condensed matter

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