Search

The metastable needlelike form of lysozyme can be obtained from continuous crystallization in mixed-suspension mixed-product-removal (MSMPR) crystallizers for conditions at which the stable tetrago...

We show that Pourbaix diagrams generated by combining first principles and tabulated experimental data can determine the electrochemical conditions needed to synthesize metastable phases in solutio...

We introduce atomate, an open-source Python framework for computational materials science simulation, analysis, and design with an emphasis on automation and extensibility. Built on top of open source Python packages already in use by the materials community such as pymatgen, FireWorks, and custodian, atomate provides well-tested workflow templates to compute various materials properties such as electronic bandstructure, elastic properties, and piezoelectric, dielectric, and ferroelectric properties. Atomate also enables the computational characterization of materials by providing workflows that calculate X-ray absorption (XAS), Electron energy loss (EELS) and Raman spectra. One of the major features of atomate is that it provides both fully functional workflows as well as reusable components that enable one to compose complex materials science workflows that use a diverse set of computational tools. Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations.

The discovery of emergent phenomena in two-dimensional (2D) materials has sparked substantial research efforts in the materials community. A significant experimental challenge for this field is exerting atomistic control over the structure and composition of the constituent 2D layers and understanding how the interactions between layers drives both structure and properties. Segregation of Pb to the surface of three bilayer thick PbSe-SnSe alloy layers was discovered within [(PbxSn1-xSe)1+δ]n(TiSe2)1 heterostructures using electron microscopy. We demonstrate that this segregation is thermodynamically favored to occur when PbxSn1-xSe layers are interdigitated with TiSe2 monolayers. Density-functional theory (DFT) calculations indicate that the observed segregation depends on what is adjacent to the PbxSn1-xSe layers. The interplay between interface and volume free energies controls both the structure and composition of the constituent layers, which can be tuned using layer thickness.

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.

We report the development of XASdb, a large database of computed reference X-ray absorption spectra (XAS), and a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 300,000 K-edge X-ray absorption near-edge spectra (XANES) for over 30,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. 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., 19 Pages, 5 Figures, 1 Table

A Materials Project based open-source Python tool, MPInterfaces, has been developed to automate the high-throughput computational screening and study of interfacial systems. The framework encompasses creation and manipulation of interface structures for solid/solid hetero-structures, solid/implicit solvents systems, nanoparticle/ligands systems; and the creation of simple system-agnostic workflows for in depth computational analysis using density-functional theory or empirical energy models. The package leverages existing open-source high-throughput tools and extends their capabilities towards the understanding of interfacial systems. We describe the various algorithms and methods implemented in the package. Using several test cases, we demonstrate how the package enables high-throughput computational screening of advanced materials, directly contributing to the Materials Genome Initiative (MGI), which aims to accelerate the discovery, development, and deployment of new materials.

First-principles calculations are used to compare the binding energies of O, OH, and F on two-dimensional, metal carbide and nitride, or MXene, surfaces in order to predict the dependence of the thermodynamic stability of these compounds on their chemical composition. Solvation effects are implicitly included in the calculations to reproduce experimental conditions as closely as possible. The results indicate that all MXene surfaces are saturated with oxygen when exposed to H2O/HF solutions at low hydrogen chemical potential, μH, and that Sc-based MXenes can also be fluorinated in solutions of higher μH. After investigating the thermodynamic stability of all 54 MXene compounds Mn+1XnO2 (M = Sc, Ti, V, Cr, Zr, Nb, Mo, Hf, Ta; X = C, N; n = 1, 2, 3), 38 are predicted to have formation energies below 200 meV/atom. Of these, six are predicted to have formation energies below 100 meV/atom, only one of which has been synthesized. Sc-based MXenes are found to be highly stable when their surfaces are terminated w...

X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique. In this work we present a database of computed XAS spectra, using the Green's formulation of the multiple scattering theory implemented in the FEFF code. With more than 500,000 K-edge X-ray absorption near edge (XANES) spectra for more than 40,000 unique materials, this database constitutes the largest existing collection of computed XAS spectra to date. The data is openly distributed via the Materials Project, enabling researchers across the world to access it for free and use it for comparisons with experiments and further analysis.

International audience; The ab-initio computational treatment of electrochemical systems requires an appropriate treatment of the solid/liquid interfaces. A fully quantum mechanical treatment of the interface is computationally demanding due to the large number of degrees of freedom involved. In this work, we describe a computationally efficient model where the electrode part of the interface is described at the density-functional theory (DFT) level, and the electrolyte part is represented through an implicit solvation model based on the Poisson-Boltzmann equation. We describe the implementation of the linearized Poisson-Boltzmann equation into the Vienna Ab-initio Simulation Package (VASP), a widely used DFT code, followed by validation and benchmarking of the method. To demonstrate the utility of the implicit electrolyte model, we apply it to study the surface energy of Cu crystal facets in an aqueous electrolyte as a function of applied electric potential. We show that the applied potential enables the control of the shape of nanocrystals from an octahedral to a truncated octahedral morphology with increasing potential.

Solid-liquid interfaces are at the heart of many modern-day technologies and provide a challenge to many materials simulation methods. A realistic first-principles computational study of such systems entails the inclusion of solvent effects. In this work, we implement an implicit solvation model that has a firm theoretical foundation into the widely used density-functional code Vienna ab initio Software Package. The implicit solvation model follows the framework of joint density functional theory. We describe the framework, our algorithm and implementation, and benchmarks for small molecular systems. We apply the solvation model to study the surface energies of different facets of semiconducting and metallic nanocrystals and the SN2 reaction pathway. We find that solvation reduces the surface energies of the nanocrystals, especially for the semiconducting ones and increases the energy barrier of the SN2 reaction.