Hydrogen Evolution Reaction of Electrodeposited Ni-W Films in Acidic Medium and Performance Optimization Using Machine Learning.

Ni-W alloy films were electrodeposited from a gluconate aqueous bath at pH=5.0, at varying current densities and temperatures. While there is little to no difference in composition, i.

Discovery of Bimetallic Hexagonal MBene MoErBT (T = O, F, and Cl).

Exfoliation from quaternary hexagonal MAB (h-MAB) phases has been suggested as a method for producing 2D in-plane ordered MBenes (i-MBenes) with the general formula (M'M″)AB. However, experimental realization of defect-free i-MBenes has not been achieved yet due to the absence of a suitable parent quaternary h-MAB phase. In this study, a machine learning (ML) model is used to predict the stability of 15771 quaternary h-MAB phases generated by considering 33 transition metals for the M site and 16 p-block elements for the A site.

Optical materials discovery and design with federated databases and machine learning.

Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches.

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.

Second-harmonic generation tensors from high-throughput density-functional perturbation theory.

Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear optical crystals are of primary importance to achieve a broader range of electromagnetic waves in the devices. However, numerous and contradicting requirements significantly limit the discovery of new potential candidates, which, in turn, hinders the technological development.

A substitutional quantum defect in WS 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 WS, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime.

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.

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.

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

This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the family of compounds with the 4/ space group. Starting from a library of 214 known 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.

Shared metadata for data-centric materials science.

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles () must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments.

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

WO is the state of the art of electrochromic oxide materials finding technological application in smart windows. In this work, a set of WO 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 LiWO films in simulated ambient conditions.

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).

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.

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.

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.

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.

Amorphization mechanism of SrIrO 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.

Discovery of multivalley Fermi surface responsible for the high thermoelectric performance in YbMnSb and YbMgSb.

The Zintl phases, Yb Sb ( = Mn, Mg, Al, Zn), are now some of the highest thermoelectric efficiency p-type materials with stability above 873 K. YbMnSb 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 YbMg Al Sb (0 ≤ ≤ 1), which enables a full mapping of the metal-to-semiconductor transition.

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.

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.

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.

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).

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 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.