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. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

Discovery of Bimetallic Hexagonal MBene Mo2ErB3T2.5 (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'2/3M″1/3)2AB2. 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. Out of these compounds, only 195 are identified as potentially stable. Subsequent high-precision first-principles calculations confirm that 47 of them exhibit both thermodynamic and dynamic stability. Their potential for exfoliation into bimetallic i-MBenes is investigated by bonding analysis. Leveraging these theoretical insights, a bimetallic i-MBene is successfully synthesized, namely 2D Mo2ErB3T2.5 (T = F, Cl and O). Further experimental scrutiny reveals its excellent performance for the hydrogen evolution reaction (HER), highlighting the application potential of bimetallic i-MBenes.

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. In the present work, the static nonlinear susceptibility and dielectric tensor are computed via density-functional perturbation theory for a set of 579 inorganic semiconductors. The computational methodology is discussed and the provided database is described with respect to both its data distribution and its format. Several comparisons with both experimental and ab initio results from literature allow to confirm the reliability of our data. The aim of this work is to provide a relevant dataset to foster the identification of promising nonlinear optical crystals in order to motivate their subsequent experimental investigation.

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.

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.

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.

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.

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.