The thermodynamic scale of inorganic crystalline metastability

Sun, Wenhao; Dacek, Stephen; Ong, Shyue Ping; Hautier, Geoffroy; Jain, Anubhav; Richards, W. Lance; Gamst, Anthony; Persson, Kristin A.; Ceder, Gerbrand

doi:10.1126/sciadv.1600225

Cited by 698 publications

(824 citation statements)

References 64 publications

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“…We excluded compounds with energy above hull (E hull ) larger than 100 meV/atom since they are not likely to be stable. 39 For C K-edge XANES of the diamond structure (Fd3m), we relaxed the constraint to 200 meV/atom as the corresponding entry (mp-66, diamond) has an E hull of 136 meV/atom. It should be noted that though the individual absorption spectrum for each symmetrically distinct site was computed for all crystal structures in the Materials Project database, the reference spectra used for comparison with the target spectra are constructed by summing these individual spectra taking into account the site multiplicities.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Resultsmentioning

confidence: 99%

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

et al. 2018

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“…30 These high cohesive energies partially originate from the 3 − valence state of solid-state nitrogen, which leads to a large electrostatic contribution to the ionic component of the lattice cohesive energy. Additionally, nitrogen has a relatively low Pauling electronegativity for an anion, allowing it to form strong covalent bonds with electropositive elements.…”

Section: ■ Nitride Thermodynamicsmentioning

confidence: 99%

Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides

et al. 2017

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Compared to oxides, the nitrides are relatively unexplored, making them a promising chemical space for novel materials discovery. Of particular interest are nitrogen-rich nitrides, which often possess useful semiconducting properties for electronic and optoelectronic applications. However, such nitrogen-rich compounds are generally metastable, and the lack of a guiding theory for their synthesis has limited their exploration. Here, we review the remarkable metastability of observed nitrides, and examine the thermodynamics of how reactive nitrogen precursors can stabilize metastable nitrogen-rich compositions during materials synthesis. We map these thermodynamic strategies onto a predictive computational search, training a data-mined ionic substitution algorithm specifically for nitride discovery, which we combine with grand-canonical DFT-SCAN phase stability calculations to compute stabilizing nitrogen chemical potentials. We identify several new nitrogen-rich binary nitrides for experimental investigation, notably the transition metal nitrides Mn 3 N 4 , Cr 3 N 4 , V 3 N 4 , and Nb 3 N 5 , the main group nitride SbN, and the pernitrides FeN 2 , CrN 2 , and Cu 2 N 2 . By formulating rational thermodynamic routes to metastable compounds, we expand the search space for functional technological materials beyond equilibrium phases and compositions.

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“…Inspired by this result, we applied a combinatorial search for stable perovskites using elements from groups 13-15 as the A site and groups 4-6 as the B site. Because of the theoretical inaccuracies of DFT calculations and the possibility of metastable phases that can be stabilized by temperature, defects, and substrates, many synthesizable inorganic crystals have positive calculated energies above hull at 0 K. Some metastable nitrides can even have energies up to 0.2 eV=atom above hull as a result of the strong bonding interactions [35]. In this work, since some of the perovskites are also nitrides, we choose to set the cutoff energy for potential synthesizability at 0.2 eV=atom.…”

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Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2018

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The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks (CGCNN) framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of DFT calculated properties for 8 different properties of crystals with various structure types and compositions after trained with 10 4 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

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The thermodynamic scale of inorganic crystalline metastability

Abstract: Data-mining the stability of 29,902 material phases reveals the thermodynamic landscape of inorganic crystalline metastability.

Cited by 698 publications

References 64 publications

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

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

Thermodynamic Routes to Novel Metastable Nitrogen-Rich Nitrides

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

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