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.

Perovskite solid solutions are screened both experimentally and through DFT to determine their redox properties for thermochemical applications.

We present a robust, automatic high-throughput workflow for the calculation of magnetic ground state of solid-state inorganic crystals, whether ferromagnetic, antiferromagnetic or ferrimagnetic, and their associated magnetic moments within the framework of collinear spin-polarized Density Functional Theory. This is done through a computationally efficient scheme whereby plausible magnetic orderings are first enumerated and prioritized based on symmetry, and then relaxed and their energies determined through conventional DFT + U calculations. This automated workflow is formalized using the atomate code for reliable, systematic use at a scale appropriate for thousands of materials and is fully customizable. The performance of the workflow is evaluated against a benchmark of 64 experimentally known mostly ionic magnetic materials of non-trivial magnetic order and by the calculation of over 500 distinct magnetic orderings. A non-ferromagnetic ground state is correctly predicted in 95% of the benchmark materials, with the experimentally determined ground state ordering found exactly in over 60% of cases. Knowledge of the ground state magnetic order at scale opens up the possibility of high-throughput screening studies based on magnetic properties, thereby accelerating discovery and understanding of new functional materials.npj Computational Materials (2019) 5:64 ; https://doi.

Structure-property relationships form the basis of many design rules in materials science, including synthesizability and long-term stability of catalysts, control of electrical and optoelectronic behavior in semiconductors, as well as the capacity of and transport properties in cathode materials for rechargeable batteries. The immediate atomic environments (i.e., the first coordination shells) of a few atomic sites are often a key factor in achieving a desired property. Some of the most frequently encountered coordination patterns are tetrahedra, octahedra, body and face-centered cubic as well as hexagonal close packedlike environments. Here, we showcase the usefulness of local order parameters to identify these basic structural motifs in inorganic solid materials by developing classification criteria. We introduce a systematic testing framework, the Einstein crystal test rig, that probes the response of order parameters to distortions in perfect motifs to validate our approach. Subsequently, we highlight three important application cases. First, we map basic crystal structure information of a large materials database in an intuitive manner by screening the Materials Project (MP) database (61,422 compounds) for elementspecific motif distributions. Second, we use the structure-motif recognition capabilities to automatically find interstitials in metals, semiconductor, and insulator materials. Our Interstitialcy Finding Tool (InFiT) facilitates high-throughput screenings of defect properties. Third, the order parameters are reliable and compact quantitative structure descriptors for characterizing diffusion hops of intercalants as our example of magnesium in MnO 2 -spinel indicates. Finally, the tools developed in our work are readily and freely available as software implementations in the pymatgen library, and we expect them to be further applied to machine-learning approaches for emerging applications in materials science.

With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.

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Dislocations are one-dimensional topological defects which occur frequently in functional thin film materials and which are known to degrade the performance of In x Ga 1-x N-based optoelectronic devices. Here, we show that large local deviations in alloy composition and atomic structure re expected to occur in and around dislocation cores in In x Ga 1-x N alloy thin 2 films. We present energy-dispersive X-ray spectroscopy data supporting this result. The methods presented here are also widely applicable for predicting composition fluctuations associated with strain fields in other inorganic functional material thin films. KEYWORDSDislocations, III-nitrides, Monte Carlo, alloy segregation, atomistic modeling, STEM-EDX MAIN TEXTDislocations are ubiquitous one-dimensional topological defects that are found within thin films of nitride semiconductors, originating at the interface with the substrate, and threading up through the active region of the device before terminating at the crystal surface 1 . These dislocations can severely degrade device efficiencies 2 , and lifetimes 3 and are responsible for a broad range of undesirable behavior such as leakage currents 4 and properties such as reduced internal quantum efficiencies 5 and defect states 6,7,8,9,10 that can act as non-radiative recombination centers. In x Ga 1-x N-based alloy semiconductors are used in light-emitting diodes 11 , laser diodes 12 and solar cells 13 , which can be tuned to emit or absorb respectively over the entire visible spectrum by varying the In composition 14 . In x Ga 1-x N is subject to very high threading dislocation densities of up to 10 11 cm -2 and typically around 10 9 cm -2 when grown by metalorganic vapourphase epitaxy 15 (MOVPE), of which the majority have a-type ('edge') or (a+c)-type ('mixed') Burgers vectors with < 1% 16 being c-type ('screw'). High dislocation densities are associated with short lifetimes in InGaN-based optoelectronic devices 17 . The electronic properties of dislocations are determined by the local bonding in the region of the dislocation core 8 . It is therefore important to determine whether or not there are local differences in the alloy composition near dislocation cores in In x Ga 1-x N. Such composition fluctuations are likely to 3 affect the electronic properties of the dislocations and would therefore affect device performance.Each dislocation is associated with a strain field determined by its Burgers vector. Since the In atom is larger than the host Ga atom, it is expected that if the In atoms are sufficiently mobile during growth, then they will segregate to the tensile part of the dislocation strain field 18 .Previous theoretical work has shown that the extreme case of a pure InN c-type dislocation core in an In x Ga 1-x N alloy is more energetically favorable compared to the equivalent In x Ga 1-x N core 19 , and also that it is favorable for In atoms to bind to a c-type dislocation core in GaN 20 . Due to the sensitivity required to detect small variations in alloy concentration on sh...