The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles

Ong, Shyue Ping; Cholia, Shreyas; Jain, Anubhav; Brafman, Miriam; Gunter, Dan; Ceder, Gerbrand; Persson, Kristin A.

doi:10.1016/j.commatsci.2014.10.037

Cited by 402 publications

(314 citation statements)

References 31 publications

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“…We have used the existing high-quality K-edge XAS spectra available in the open EELS Data Base (EELSDb) 8 as reference data, and matched them with the corresponding materials in the Materials Project 12 using the Materials API 41 and pymatgen. 13 For materials in the EELSDb without structural information, ground state structures with identical chemical compositions in the Materials Project were used.…”

Section: Benchmarking Detailsmentioning

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: Benchmarking Detailsmentioning

confidence: 99%

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

et al. 2018

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“…DFT relaxations that resulted in "phase-separated" unit cells with metal slabs and N 2 molecules were removed from the data set. Phase stability calculations are computed using the phase diagram analysis package in Pymatgen, 63 calculated with respect to known nitride phases from the Materials Project, 49 attained using the Materials Project REST API, 64 and with total energies recalculated in SCAN. 2 for V 3 N 4 is calculated with respect to the rock-salt VN, which is the stable phase under ambient conditions.…”

Section: ■ Methodsmentioning

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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“…The compounds included in the dataset originate from the Inorganic Crystal Structure Database (ICSD) and were downloaded from the Materials Project (MP) 21 using their API. 22 Full details of how the dataset was rened can be found in the Methods section. In broad terms, all the compounds meet the following criteria (total number of compounds remaining in the dataset shown in brackets):…”

Section: A Data Curationmentioning

confidence: 99%

Materials discovery by chemical analogy: role of oxidation states in structure prediction

et al. 2018

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The likelihood of an element to adopt a specific oxidation state in a solid, given a certain set of neighbours, might often be obvious to a trained chemist. However, encoding this information for use in high-throughput searches presents a significant challenge. We carry out a statistical analysis of the occurrence of oxidation states in 16 735 ordered, inorganic compounds and show that a large number of cations are only likely to exhibit certain oxidation states in combination with particular anions. We use this data to build a model that ascribes probabilities to the formation of hypothetical compounds, given the proposed oxidation states of their constituent species. The model is then used as part of a high-throughput materials design process, which significantly narrows down the vast compositional search space for new ternary metal halide compounds. Finally, we employ a machine learning analysis of existing compounds to suggest likely structures for a small subset of the candidate compositions. We predict two new compounds, MnZnBr 4 and YSnF 7 , that are thermodynamically stable according to density functional theory, as well as four compounds, MnCdBr 4 , MnRu 2 Br 8 , ScZnF 5 and ZnCoBr 4 , which lie within the window of metastability.

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The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles

Cited by 402 publications

References 31 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

Materials discovery by chemical analogy: role of oxidation states in structure prediction

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