Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide.

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO2) profoundly modifies the ground state properties. The electrical behavior of HxVO2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. In this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a HxVO2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information sciences.

Synthesis of Quantum-Confined Borophene Nanoribbons.

Borophene nanoribbons (BNRs) are one-dimensional strips of atomically thin boron expected to exhibit quantum-confined electronic properties that are not present in extended two-dimensional borophene. While the parent material borophene has been experimentally shown to possess anisotropic metallicity and diverse polymorphic structures, the atomically precise synthesis of nanometer-wide BNRs has not yet been achieved. Here, we demonstrate the synthesis of multiple BNR polymorphs with well-defined edge configurations within the nanometer-scale terraces of vicinal Ag(977). Through atomic-scale imaging, spectroscopy, and first-principles calculations, the synthesized BNR polymorphs are characterized and found to possess distinct edge structures and electronic properties. For single-phase BNRs, v1/6-BNRs and v1/5-BNRs adopt reconstructed armchair edges and sawtooth edges, respectively. In addition, the electronic properties of single-phase v1/6-BNRs and v1/5-BNRs are dominated by Friedel oscillations and striped moiré patterns, respectively. On the other hand, mixed-phase BNRs possess quantum-confined states with increasing nodes in the electronic density of states at elevated biases. Overall, the high degree of polymorphism and diverse edge topologies in borophene nanoribbons provide a rich quantum platform for studying one-dimensional electronic states.

van der Waals Epitaxy, Superlubricity, and Polarization of the 2D Ferroelectric SnS.

Tin monosulfide (SnS) is a two-dimensional layered semiconductor that exhibits in-plane ferroelectric order at very small thicknesses and is of interest in highly scaled devices. Here we report the epitaxial growth of SnS on hexagonal boron nitride (hBN) using a pulsed metal-organic chemical vapor deposition process. Lattice matching is observed between the SnS(100) and hBN{11̅0} planes, with no evidence of strain. Atomic force microscopy reveals superlubricity along the commensurate direction of the SnS/hBN interface, and first-principles calculations suggest that friction is controlled by the edges of the SnS islands, rather than interface interactions. Differential phase contrast imaging detects remnant polarization in SnS islands with domains that are not dictated by step-edges in the SnS. The growth of ferroelectric SnS on high quality hBN substrates is a promising step toward electrically switchable ferroelectric semiconducting devices.

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets.

This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy dataset containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.

The AXEAP2 program for Kß X-ray emission spectra analysis using artificial intelligence.

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kß XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d-3p electron exchange force and Kß emission core-hole lifetime.

Discovery of chalcogenides structures and compositions using mixed fluxes.

Advancements in many modern technologies rely on the continuous need for materials discovery. However, the design of synthesis routes leading to new and targeted solid-state materials requires understanding of reactivity patterns1-3. Advances in synthesis science are necessary to increase efficiency and accelerate materials discovery4-10. We present a highly effective methodology for the rational discovery of materials using high-temperature solutions or fluxes having tunable solubility. This methodology facilitates product selection by projecting the free-energy landscape into real synthetic variables: temperature and flux ratio. We demonstrate the effectiveness of this technique by synthesizing compounds in the chalcogenide system of A(Ba)-Cu-Q(O) (Q = S or Se; A = Na, K or Rb) using mixed AOH/AX (A = Li, Na, K or Rb; X = Cl or I) fluxes. We present 30 unreported compounds or compositions, including more than ten unique structural types, by systematically varying the temperature and flux ratios without requiring changing the proportions of starting materials. Also, we found that the structural dimensionality of the compounds decreases with increasing reactant solubility and temperature. This methodology serves as an effective general strategy for the rational discovery of inorganic solids.

AXEAP: a software package for X-ray emission data analysis using unsupervised machine learning.

The Argonne X-ray Emission Analysis Package (AXEAP) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS.

Universal machine learning framework for defect predictions in zinc blende semiconductors.

We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities. Optimized kernel ridge, Gaussian process, random forest, and neural network regression models are applied to screen impurities with lower formation energy than dominant native defects in all compounds.

Ingrained: An Automated Framework for Fusing Atomic-Scale Image Simulations into Experiments.

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy, an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, "ingrained," an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond, is introduced. Herein, the overall "ingrained" workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation are described.

Understanding, discovery, and synthesis of 2D materials enabled by machine learning.

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets.

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems.

Mechanistic Investigation of Molybdenum Disulfide Defect Photoluminescence Quenching by Adsorbed Metallophthalocyanines.

Lattice defects play an important role in determining the optical and electrical properties of monolayer semiconductors such as MoS2. Although the structures of various defects in monolayer MoS2 are well studied, little is known about the nature of the fluorescent defect species and their interaction with molecular adsorbates. In this study, the quenching of the low-temperature defect photoluminescence (PL) in MoS2 is investigated following the deposition of metallophthalocyanines (MPcs). The quenching is found to significantly depend on the identity of the phthalocyanine metal, with the quenching efficiency decreasing in the order CoPc > CuPc > ZnPc, and almost no quenching by metal-free H2Pc is observed. Time-correlated single photon counting (TCSPC) measurements corroborate the observed trend, indicating a decrease in the defect PL lifetime upon MPc adsorption, and the gate voltage-dependent PL reveals the suppression of the defect emission even at large Fermi level shifts. Density functional theory modeling argues that the MPc complexes stabilize dark negatively charged defects over luminescent neutral defects through an electrostatic local gating effect. These results demonstrate the control of defect-based excited-state decay pathways via molecular electronic structure tuning, which has broad implications for the design of mixed-dimensional optoelectronic devices.

First principles modeling of polymer encapsulant degradation in Si photovoltaic modules.

An outstanding issue in the longevity of photovoltaic (PV) modules is the accelerated degradation caused by the presence of moisture. Moisture leads to interfacial instability, de-adhesion, encapsulant decomposition, and contact corrosion. However, experimental characterization of moisture in PV modules is not trivial and its impacts can take years or decades to establish in the field, presenting a major obstacle to designing high-reliability modules. First principles calculations provide an alternative way to study the ingress of water and its detrimental effect on the structure and decomposition of the polymer encapsulant and interfaces between the encapsulant and the semiconductor, the metal contacts, or the dielectric layer. In this work, we use density functional theory (DFT) computations to model single chain, crystalline and cross-linked structures, infrared (IR) signatures, and degradation mechanisms of ethylene vinyl acetate (EVA), the most common polymer encapsulant used in Si PV modules. IR-active modes computed for low energy EVA structures and possible decomposition products match well with reported experiments. The EVA decomposition energy barriers computed using the Nudged Elastic Band (NEB) method show a preference for acetic acid formation as compared to acetaldehyde, are lowered in the presence of a water solvent or hydroxyl ion catalyst, and match well with reported experimental activation energies. This systematic study leads to a clear picture of the hydrolysis-driven decomposition of EVA in terms of energetically favorable mechanisms, possible intermediate structures, and IR signatures of reactants and products.

Synthesis of borophane polymorphs through hydrogenation of borophene.

Synthetic two-dimensional polymorphs of boron, or borophene, have attracted attention because of their anisotropic metallicity, correlated-electron phenomena, and diverse superlattice structures. Although borophene heterostructures have been realized, ordered chemical modification of borophene has not yet been reported. Here, we synthesize "borophane" polymorphs by hydrogenating borophene with atomic hydrogen in ultrahigh vacuum. Through atomic-scale imaging, spectroscopy, and first-principles calculations, the most prevalent borophane polymorph is shown to possess a combination of two-center-two-electron boron-hydrogen and three-center-two-electron boron-hydrogen-boron bonds. Borophane polymorphs are metallic with modified local work functions and can be reversibly returned to pristine borophene through thermal desorption of hydrogen. Hydrogenation also provides chemical passivation because borophane reduces oxidation rates by more than two orders of magnitude after ambient exposure.

Spectroscopic Comparison of Thermal Transport at Organic-Inorganic and Organic-Hybrid Interfaces Using CsPbBr3 and FAPbBr3 (FA = Formamidinium) Perovskite Nanocrystals.

Thermal transport across interfaces depends on the matching of vibrational structure at the interface. This work examines the transfer of thermal excitation from an organic ligand coating to either all-inorganic cesium lead tribromide (CsPbBr3) nanocrystals or hybrid organic-inorganic formamidinium lead tribromide (FAPbBr3) nanocrystals using selective infrared optical excitation. These two semiconductors are directly compared because they (or similar semiconductors) are currently envisioned as strong candidates in many optoelectronic technologies and they differ due to the presence of an organic or inorganic cation, which introduces substantial differences in the phonon density of states in otherwise quite similar semiconductors. Infrared excitation of C-H vibrations of surface-bound ligands generates a temperature gradient between the organic ligand shell and nanocrystal core, which results in heat flow, measured by probing changes of the semiconductor band gap. Heat transfer to both perovskite compositions of comparable sizes is similar (25-30 ps), due to fast intramolecular vibrational relaxation and similar matching of low-energy phonons with the organic ligand, but FAPbBr3 samples show a slow bleaching kinetic on the order of 1 ns. This slow, heat-induced change in the semiconductor band gap is attributed not to interfacial heat transfer but instead to thermal equilibration between the organic and inorganic sublattices of FAPbBr3. Ab initio molecular dynamics simulations support the hypothesis that low-energy inorganic sublattice phonon modes are populated initially in the heat transfer process, with a slow thermal population of the higher-energy phonon modes associated primarily with the organic cation. Slow thermal equilibration of FAPbBr3 is likely to substantially impact the time-dependent response of optoelectronic devices that heat the semiconductor active layer and provide further evidence that the poor bulk thermal transport of hybrid perovskite materials extends to microscopic thermal processes.

Stabilization of a monolayer tellurene phase at CdTe interfaces.

Two-dimensional (2D) materials provide a plethora of novel condensed matter physics and are the new playground in materials science, offering potentially vast applications. One of the critical hurdles for many 2D systems is the synthesis of these low-dimensional systems as well as the prediction and identification of new candidates. Herein, a self-assembly of a monolayer tellurene by bonding CdTe wafers is demonstrated for the first time. The conventional applications of wafer-bonding range from the production of microelectromechanical systems to the synthesis of lattice-mismatched multi-junction photovoltaics. Due to the heterogeneous materials that are typically employed, the bond-interface usually contains a thin amorphous layer or arrays of dislocations. Such an interface is thus itself inactive and in many cases has detrimental effects on the device. The new material phase stabilized in this work consists of an undulating monolayer of tellurium atoms covalently bonded to {111} Cd-terminated CdTe wafer surfaces. First-principles calculations and experimentally observed changes in the localized plasmon excitation energy indicate the clear rearrangement of the underlying band-structure suggesting a metallic character, bands showing linear dispersion, and a significant asymmetric spin-band splitting. The I-V characteristics show the presence of a highly conductive pathway that lowers the resistivity by three orders of magnitude, as compared to bulk CdTe, which can be attributed to the tellurium monolayer. The findings indicate that suitably chosen crystallographic wafer surfaces can act as structural templates allowing the production of exotic phases. The presently stabilized monolayer is an addition to the family of tellurene variants, providing new insights into the fundamental properties of this and other emerging 2D materials, while attracting attention to the unusual side of the wafer-bonding technology exemplified in this study.

Understanding the Role of Overpotentials in Lithium Ion Conversion Reactions: Visualizing the Interface.

Oxide conversion reactions are known to have substantially higher specific capacities than intercalation materials used in Li-ion batteries, but universally suffer from large overpotentials associated with the formation of interfaces between the resulting nanoscale metal and Li2O products. Here we use the interfacial sensitivity of operando X-ray reflectivity to visualize the structural evolution of ultrathin NiO electrodes and their interfaces during conversion. We observe two additional reactions prior to the well-known bulk, three-dimensional conversion occurring at 0.6 V: an accumulation of lithium at the buried metal/oxide interface (at 2.2 V) followed by interfacial lithiation of the buried NiO/Ni interface at the theoretical potential for conversion (at 1.9 V). To understand the mechanisms for bulk and interfacial lithiation, we calculate interfacial energies using density functional theory to build a potential-dependent nucleation model for conversion. These calculations show that the additional space charge layer of lithium is a crucial component for reducing energy barriers for conversion in NiO.

Atomistic manipulation of reversible oxidation and reduction in Ag with an electron beam.

Employing electrons for direct control of a nanoscale reaction is highly desirable since it enables fabrication of nanostructures with different properties at atomic resolution and with flexibility of dimensions and location. Here, applying in situ transmission electron microscopy, we show the reversible oxidation and reduction kinetics in Ag, well controlled by changing the dose rate of the electron beam. Aberration-corrected high-resolution transmission electron microscopy observation reveals that O atoms are preferably inserted and extracted along the {111} close-packed planes of Ag, leading to the nucleation and decomposition of nanoscale Ag2O islands on the Ag substrate. By controlling the electron beam size and dose rate, we demonstrated the fabrication of an array of 3 nm Ag2O nanodots in an Ag matrix. Our results open a new pathway to manipulate an atomistic reaction with an electron beam towards the precise fabrication of nanostructures for device applications.