The Role of Alkalis in Orchestrating Uranyl-Peroxide Reactivity Leading to Direct Air Capture of Carbon Dioxide.

Spectator ions have known and emerging roles in aqueous metal-cation chemistry, respectively directing solubility, speciation, and reactivity. Here, we isolate and structurally characterize the last two metastable members of the alkali uranyl triperoxide series, the Rb+ and Cs+ salts (Cs-U1 and Rb-U1). We document their rapid solution polymerization via small-angle X-ray scattering, which is compared to the more stable Li+, Na+ and K+ analogues. To understand the role of the alkalis, we also quantify alkali-hydroxide promoted peroxide deprotonation and decomposition, which generally exhibits increasing reactivity with increasing alkali size. Cs-U1, the most unstable of the uranyl triperoxide monomers, undergoes ambient direct air capture of CO2 in the solid-state, converting to Cs4[UVIO2(CO3)3], evidenced by single-crystal X-ray diffraction, transmission electron microscopy, and Raman spectroscopy. We have attempted to benchmark the evolution of Cs-U1 to uranyl tricarbonate, which involves a transient, unstable hygroscopic solid that contains predominantly pentavalent uranium, quantified by X-ray photoelectron spectroscopy. Powder X-ray diffraction suggests this intermediate state contains a hydrous derivative of CsUVO3, where the parent phase has been computationally predicted, but not yet synthesized.

Dislocations in AlGaN: Core Structure, Atom Segregation, and Optical Properties.

We conducted a comprehensive investigation of dislocations in Al0.46Ga0.54N. Using aberration-corrected scanning transmission electron microscopy and energy dispersive X-ray spectroscopy, the atomic structure and atom distribution at the dislocation core have been examined. We report that the core configuration of dislocations in AlGaN is consistent with that of other materials in the III-Nitride system. However, we observed that the dissociation of mixed-type dislocations is impeded by alloying GaN with AlN, which is confirmed by our experimental observation of Ga and Al atom segregation in the tensile and compressive parts of the dislocations, respectively. Investigation of the optical properties of the dislocations shows that the atom segregation at dislocations has no significant effect on the intensity recorded by cathodoluminescence in the vicinity of the dislocations. These results are in contrast with the case of dislocations in In0.09Ga0.91N where segregation of In and Ga atoms also occurs but results in carrier localization limiting non-radiative recombination at the dislocation. This study therefore sheds light on why InGaN-based devices are generally more resilient to dislocations than their AlGaN-based counterparts.

Segregation of In to dislocations in InGaN.

Dislocations are one-dimensional topological defects that occur frequently in functional thin film materials and that are known to degrade the performance of InxGa1-xN-based optoelectronic devices. Here, we show that large local deviations in alloy composition and atomic structure are expected to occur in and around dislocation cores in InxGa(1-x)N alloy thin 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.

Erratum: A method to computationally screen for tunable properties of crystalline alloys.

[This corrects the article DOI: 10.1016/j.patter.2023.100723.].

A method to computationally screen for tunable properties of crystalline alloys.

Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, but, in contrast, many real functional materials are heavily engineered mixtures of compounds rather than single bulk compounds. We present a framework and open-source code to automatically construct and analyze possible alloys and solid solutions from a set of existing experimental or calculated ordered compounds, without requiring additional metadata except crystal structure. As a demonstration, we apply this framework to all compounds in the Materials Project to create a new, publicly available database of >600,000 unique "alloy pair" entries that can be used to search for materials with tunable properties. We exemplify this approach by searching for transparent conductors and reveal candidates that might have been excluded in a traditional screening. This work lays a foundation from which materials databases can go beyond stoichiometric compounds and approach a more realistic description of compositionally tunable materials.

A representation-independent electronic charge density database for crystalline materials.

In addition to being the core quantity in density-functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With the increasing prevalence of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.

Big Data in a Nano World: A Review on Computational, Data-Driven Design of Nanomaterials Structures, Properties, and Synthesis.

The recent rise of computational, data-driven research has significant potential to accelerate materials discovery. Automated workflows and materials databases are being rapidly developed, contributing to high-throughput data of bulk materials that are growing in quantity and complexity, allowing for correlation between structural-chemical features and functional properties. In contrast, computational data-driven approaches are still relatively rare for nanomaterials discovery due to the rapid scaling of computational cost for finite systems. However, the distinct behaviors at the nanoscale as compared to the parent bulk materials and the vast tunability space with respect to dimensionality and morphology motivate the development of data sets for nanometric materials. In this review, we discuss the recent progress in data-driven research in two aspects: functional materials design and guided synthesis, including commonly used metrics and approaches for designing materials properties and predicting synthesis routes. More importantly, we discuss the distinct behaviors of materials as a result of nanosizing and the implications for data-driven research. Finally, we share our perspectives on future directions for extending the current data-driven research into the nano realm.

Addressing the critical need for open experimental databases in materials science.

With the HTEM, an open online database containing experimental synthesis and characterization data of thin film inorganic materials, Talley et al. (2021) lay a foundation for a new era of high-throughput materials design.

Database of ab initio L-edge X-ray absorption near edge structure.

The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.

Metastable Ta2N3 with highly tunable electrical conductivity via oxygen incorporation.

The binary Ta-N chemical system includes several compounds with notable prospects in microelectronics, solar energy harvesting, and catalysis. Among these, metallic TaN and semiconducting Ta3N5 have garnered significant interest, in part due to their synthetic accessibility. However, tantalum sesquinitride (Ta2N3) possesses an intermediate composition and largely unknown physical properties owing to its metastable nature. Herein, Ta2N3 is directly deposited by reactive magnetron sputtering and its optoelectronic properties are characterized. Combining these results with density functional theory provides insights into the critical role of oxygen in both synthesis and electronic structure. While the inclusion of oxygen in the process gas is critical to Ta2N3 formation, the resulting oxygen incorporation in structural vacancies drastically modifies the free electron concentration in the as-grown material, thus leading to a semiconducting character with a 1.9 eV bandgap. Reducing the oxygen impurity concentration via post-synthetic ammonia annealing increases the conductivity by seven orders of magnitude and yields the metallic characteristics of a degenerate semiconductor, consistent with theoretical predictions. Thus, this inverse oxygen doping approach - by which the carrier concentration is reduced by the oxygen impurity - offers a unique opportunity to tailor the optoelectronic properties of Ta2N3 for applications ranging from photochemical energy conversion to advanced photonics.

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

High-throughput search for magnetic and topological order in transition metal oxides.

The discovery of intrinsic magnetic topological order in MnBi2Te4 has invigorated the search for materials with coexisting magnetic and topological phases. These multiorder quantum materials are expected to exhibit new topological phases that can be tuned with magnetic fields, but the search for such materials is stymied by difficulties in predicting magnetic structure and stability. Here, we compute more than 27,000 unique magnetic orderings for more than 3000 transition metal oxides in the Materials Project database to determine their magnetic ground states and estimate their effective exchange parameters and critical temperatures. We perform a high-throughput band topology analysis of centrosymmetric magnetic materials, calculate topological invariants, and identify 18 new candidate ferromagnetic topological semimetals, axion insulators, and antiferromagnetic topological insulators. To accelerate future efforts, machine learning classifiers are trained to predict both magnetic ground states and magnetic topological order without requiring first-principles calculations.