A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation.

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co S 0 ) and fabricate it with scanning tunneling microscopy (STM). The Co S 0 electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

First-Principles Investigation of the Optical Properties of Eumelanin Protomolecules.

Using first-principles calculations, we investigate the absorption spectra (in the near-infrared, visible, and first UV range) of the two most probable eumelanin tetrameric molecules exhibiting either a linear open-chain or a cyclic porphyrine-like configuration. In order to simulate a realistic molecular system, an implicit solvent model is used in our calculations to mimic the effect of the solvated environment around the eumelanin molecule. Although the presence of solvent is found not to significantly affect the absorption pattern of both molecules, the onset of the spectra are shifted toward higher energies, especially for the linear tetramer. Interestingly, the absorption spectra and optical onsets of the two molecules differ significantly both in a vacuum and in ethanol. However, the two predicted spectra do not allow us to definitely discriminate between the two configurations when comparing the theoretical predictions with the available experimental spectrum. In addition, a mix of the two eumelanin configurations (close to fifty-fifty) leads to a maximum overlap between theoretical and experimental spectra. Consequently, this theoretical research shows that deeper insight can be gained using beyond DFT techniques on the real form of eumelanin protomolecules present in living systems as well as on their possible use in hybrid solar cells.

Limits to Hole Mobility and Doping in Copper Iodide.

Over one hundred years have passed since the discovery of the p-type transparent conducting material copper iodide, predating the concept of the "electron-hole" itself. Supercentenarian status notwithstanding, little is understood about the charge transport mechanisms in CuI. Herein, a variety of modeling techniques are used to investigate the charge transport properties of CuI, and limitations to the hole mobility over experimentally achievable carrier concentrations are discussed. Poor dielectric response is responsible for extensive scattering from ionized impurities at degenerately doped carrier concentrations, while phonon scattering is found to dominate at lower carrier concentrations. A phonon-limited hole mobility of 162 cm2 V-1 s-1 is predicted at room temperature. The simulated charge transport properties for CuI are compared to existing experimental data, and the implications for future device performance are discussed. In addition to charge transport calculations, the defect chemistry of CuI is investigated with hybrid functionals, revealing that reasonably localized holes from the copper vacancy are the predominant source of charge carriers. The chalcogens S and Se are investigated as extrinsic dopants, where it is found that despite relatively low defect formation energies, they are unlikely to act as efficient electron acceptors due to the strong localization of holes and subsequent deep transition levels.

Machine Learning-Accelerated Discovery of A2BC2 Ternary Electrides with Diverse Anionic Electron Densities.

This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A2BC2 family of compounds with the P4/mbm space group. Starting from a library of 214 known A2BC2 phases, density functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this data set and used to predict the electride behavior of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations. Through this tiered approach, 41 stable and 104 metastable new A2BC2 electrides were predicted. Interestingly, all three kinds of electrides, i.e., electron-deficient, electron-neutral, and electron-rich electrides, are present in the set of predicted compounds. Three of the most promising new electrides (two electron-rich, Nd2ScSi2 and La2YbGe2, and one electron-deficient Y2LiSi2) were then successfully synthesized and characterized experimentally. Furthermore, the synthesized electrides were found to exhibit high catalytic activities for NH3 synthesis under mild conditions when Ru-loaded. The electron-deficient Y2LiSi2, in particular, was seen to exhibit a good balance of catalytic activity and chemical stability, suggesting its future application in catalysis.

Toward the Prediction of Electrochromic Properties of WO3 Films: Combination of Experimental and Machine Learning Approaches.

WO3 is the state of the art of electrochromic oxide materials finding technological application in smart windows. In this work, a set of WO3 thin films were deposited by magnetron sputtering by varying total pressure, oxygen partial pressure, and power. On each film two properties were measured, the electrochemical reversibility and the blue color persistence of LixWO3 films in simulated ambient conditions. With the help of machine learning, prediction maps for such electrochromic properties, namely, color persistence and reversibility, were designed. High-performance WO3 films were targeted by a global score which is the product of these two properties. The combined approach of experimental measurements and machine learning led to a complete picture of electrochromic properties depending of sputtering parameters providing an efficient tool in regards to time saving.

Automated Bonding Analysis with Crystal Orbital Hamilton Populations.

Invited for this month's cover are researchers from Bundesanstalt für Materialforschung und -prüfung (Federal Institute for Materials Research and Testing) in Germany, Friedrich Schiller University Jena, Université catholique de Louvain, University of Oregon, Science & Technology Facilities Council, RWTH Aachen University, Hoffmann Institute of Advanced Materials, and Dartmouth College. The cover picture shows a workflow for automatic bonding analysis with Python tools (green python). The bonding analysis itself is performed with the program LOBSTER (red lobster). The starting point is a crystal structure, and the results are automatic assessments of the bonding situation based on Crystal Orbital Hamilton Populations (COHP), including automatic plots and text outputs. Coordination environments and charges are also assessed. More information can be found in the Research Article by J. George, G. Hautier, and co-workers.

Automated Bonding Analysis with Crystal Orbital Hamilton Populations.

Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. We use the program packages VASP and LOBSTER, and the Python packages atomate and pymatgen. The analysis produced by our tools includes plots, a textual description, and key data in a machine-readable format. To illustrate those capabilities, we have selected simple test compounds (NaCl, GaN), the oxynitrides BaTaO2 N, CaTaO2 N, and SrTaO2 N, and the thermoelectric material Yb14 Mn1 Sb11 . We show correlations between bond strengths and stabilities in the oxynitrides and the influence of the Mn-Sb bonds on the magnetism in Yb14 Mn1 Sb11 . Our contribution enables high-throughput bonding analysis and will facilitate the use of bonding information for machine learning studies.

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.

Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet.

As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, while closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10 000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter.

Ferroelectricity and multiferroicity in anti-Ruddlesden-Popper structures.

Combining ferroelectricity with other properties such as visible light absorption or long-range magnetic order requires the discovery of new families of ferroelectric materials. Here, through the analysis of a high-throughput database of phonon band structures, we identify a structural family of anti-Ruddlesden-Popper phases [Formula: see text]O (A=Ca, Sr, Ba, Eu, X=Sb, P, As, Bi) showing ferroelectric and antiferroelectric behaviors. The discovered ferroelectrics belong to the new class of hyperferroelectrics that polarize even under open-circuit boundary conditions. The polar distortion involves the movement of O anions against apical A cations and is driven by geometric effects resulting from internal chemical strains. Within this structural family, we show that [Formula: see text]O combines coupled ferromagnetic and ferroelectric order at the same atomic site, a very rare occurrence in materials physics.

Discovery of multivalley Fermi surface responsible for the high thermoelectric performance in Yb14MnSb11 and Yb14MgSb11.

The Zintl phases, Yb14 MSb11 (M = Mn, Mg, Al, Zn), are now some of the highest thermoelectric efficiency p-type materials with stability above 873 K. Yb14MnSb11 gained prominence as the first p-type thermoelectric material to double the efficiency of SiGe alloy, the heritage material in radioisotope thermoelectric generators used to power NASA's deep space exploration. This study investigates the solid solution of Yb14Mg1-x Al x Sb11 (0 ≤ x ≤ 1), which enables a full mapping of the metal-to-semiconductor transition. Using a combined theoretical and experimental approach, we show that a second, high valley degeneracy (N v = 8) band is responsible for the groundbreaking performance of Yb14 MSb11 This multiband understanding of the properties provides insight into other thermoelectric systems (La3-x Te4, SnTe, Ag9AlSe6, and Eu9CdSb9), and the model predicts that an increase in carrier concentration can lead to zT > 1.5 in Yb14 MSb11 systems.

Amorphization mechanism of SrIrO3 electrocatalyst: How oxygen redox initiates ionic diffusion and structural reorganization.

The use of renewable electricity to prepare materials and fuels from abundant molecules offers a tantalizing opportunity to address concerns over energy and materials sustainability. The oxygen evolution reaction (OER) is integral to nearly all material and fuel electrosyntheses. However, very little is known about the structural evolution of the OER electrocatalyst, especially the amorphous layer that forms from the crystalline structure. Here, we investigate the interfacial transformation of the SrIrO3 OER electrocatalyst. The SrIrO3 amorphization is initiated by the lattice oxygen redox, a step that allows Sr2+ to diffuse and O2- to reorganize the SrIrO3 structure. This activation turns SrIrO3 into a highly disordered Ir octahedral network with Ir square-planar motif. The final Sr y IrO x exhibits a greater degree of disorder than IrO x made from other processing methods. Our results demonstrate that the structural reorganization facilitated by coupled ionic diffusions is essential to the disordered structure of the SrIrO3 electrocatalyst.

Electron-Phonon beyond Fröhlich: Dynamical Quadrupoles in Polar and Covalent Solids.

We include the treatment of quadrupolar fields beyond the Fröhlich interaction in the first-principles electron-phonon vertex in semiconductors. Such quadrupolar fields induce long-range interactions that have to be taken into account for accurate physical results. We apply our formalism to Si (nonpolar), GaAs, and GaP (polar) and demonstrate that electron mobilities show large errors if dynamical quadrupoles are not properly treated.

Solving Chemistry Problems via an End-to-End Approach: A Proof of Concept.

Traditionally, chemistry problems are solved by means of a deductive approach. The question to be addressed is typically related to the value of a property that is either measured experimentally, computed using quantum-chemistry software, or (more recently) predicted using a machine-learned model. In this paper, we demonstrate that an inductive approach can be adopted using End-to-End (E2E) machine learning. This approach is illustrated for tackling the following chemistry problems: (i) determine the fully coordinated (FC) and undercoordinated (UC) atoms in a molecule with one missing atom, (ii) identify the type of atom that is missing in such an incomplete molecule, and (iii) predict the direction of a reaction between two molecules according to an existing dataset. The E2E approach leads to accuracies higher than 99%, 98%, and 93% for these three problems, respectively. Finally, in order to achieve such accuracies, a descriptor for the molecules, called bag of clusters, is introduced and compared with a series previously proposed descriptors, highlighting a series of advantages.

ChemEnv: a fast and robust coordination environment identification tool.

Coordination or local environments have been used to describe, analyze and understand crystal structures for more than a century. Here, a new tool called ChemEnv, which can identify coordination environments in a fast and robust manner, is presented. In contrast to previous tools, the assessment of the coordination environments is not biased by small distortions of the crystal structure. Its robust and fast implementation enables the analysis of large databases of structures. The code is available open source within the pymatgen package and the software can also be used through a web app available on http://crystaltoolkit.org through the Materials Project.

Li diffusion in Si and LiSi: Nuclear quantum effects and anharmonicity.

The diffusion of Li in bulk Si and crystalline LiSi is investigated over a wide range of temperatures employing first-principles calculations based on density functional theory, transition state theory, and the kinetic Monte Carlo method. Nuclear quantum effects are incorporated by computing the vibrational spectrum and its effect on the effective energy barrier. The Li diffusion coefficient in bulk Si calculated with such quantum effects is ∼33% lower than the classical limit near room temperature due to higher effective energy barrier and tends to the classical limit at a high temperature (>1000 K). The presence of anharmonicity, estimated by the quasiharmonic approximation and the cBΩ model, increases the diffusion coefficient by ∼60%. For Li diffusion in LiSi with multiple vacancy jumps, we obtain an effective diffusion barrier of 0.27 eV ± 0.01 eV. In the Li-Si system, the quantum mechanical effects are only marginally significant at room temperature.

ABINIT: Overview and focus on selected capabilities.

abinit is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe-Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the "temperature-dependent effective potential" approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which abinit relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The abinit DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library libpaw. abinit has strong links with many other software projects that are briefly mentioned.

The Limited Predictive Power of the Pauling Rules.

The Pauling rules have been used for decades to rationalise the crystal structures of ionic compounds. Despite their importance, there has been no statistical assessment of the performances of these five empirical rules so far. Here, we rigorously and automatically test all five Pauling rules for a large data set of around 5000 known oxides. We discuss each Pauling rule separately, stressing their limits and range of application in terms of chemistries and structures. We conclude that only 13 % of the oxides simultaneously satisfy the last four rules, indicating a much lower predictive power than expected.

High-throughput density-functional perturbation theory phonons for inorganic materials.

The knowledge of the vibrational properties of a material is of key importance to understand physical phenomena such as thermal conductivity, superconductivity, and ferroelectricity among others. However, detailed experimental phonon spectra are available only for a limited number of materials, which hinders the large-scale analysis of vibrational properties and their derived quantities. In this work, we perform ab initio calculations of the full phonon dispersion and vibrational density of states for 1521 semiconductor compounds in the harmonic approximation based on density functional perturbation theory. The data is collected along with derived dielectric and thermodynamic properties. We present the procedure used to obtain the results, the details of the provided database and a validation based on the comparison with experimental data.