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.

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.

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.

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.

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.

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.

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.

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.

Atomistic determination of the surface structure of Cu2O(111): experiment and theory.

Cuprous oxide (Cu2O) is a promising catalyst for several important reactions. However, the atomic structures of defective Cu2O surfaces, which critically affect the catalytic properties both thermodynamically and kinetically, are not unambiguously characterized. High-resolution scanning tunneling microscopy (STM), combined with density functional theory (DFT) calculations and STM simulations, has been used to determine the atomic structure of the (111) surface of a Cu2O bulk crystal. The single crystal surface, processed by ultrahigh vacuum cleaning and oxygen annealing, shows a (1 × 1) periodicity in the low-energy electron diffraction pattern. The pristine (defect-free) Cu2O(111) surface exhibits a lattice of protrusions with hexagonal symmetry under STM, which is attributed to the dangling bonds of the coordinatively unsaturated copper (CuU) atoms on the surface. Two types of surface atomic defects are also identified, including the CuU vacancy and the oxygen-vacancy-induced local surface restructuring. The electronic structure of this surface measured by dI/dV spectroscopy shows an energy band gap of ∼1.6-2.1 eV. Consistent with dI/dV measurements, DFT calculations identified surface states within the electronic band gap arising from the Cu ions on the surface. Our results provide a clear picture of the pristine and defective Cu2O(111) surface structure in addition to the formation mechanism of the reconstructed surface, paving the way toward studying the site-dependent reactivity of this surface.

Theory of Thermal Relaxation of Electrons in Semiconductors.

We compute the transient dynamics of phonons in contact with high energy "hot" charge carriers in 12 polar and nonpolar semiconductors, using a first-principles Boltzmann transport framework. For most materials, we find that the decay in electronic temperature departs significantly from a single-exponential model at times ranging from 1 to 15 ps after electronic excitation, a phenomenon concomitant with the appearance of nonthermal vibrational modes. We demonstrate that these effects result from slow thermalization within the phonon subsystem, caused by the large heterogeneity in the time scales of electron-phonon and phonon-phonon interactions in these materials. We propose a generalized two-temperature model accounting for phonon thermalization as a limiting step of electron-phonon thermalization, which captures the full thermal relaxation of hot electrons and holes in semiconductors. A direct consequence of our findings is that, for semiconductors, information about the spectral distribution of electron-phonon and phonon-phonon coupling can be extracted from the multiexponential behavior of the electronic temperature.

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.

On the origin of photoluminescence in silicon nanocrystals: pressure-dependent structural and optical studies.

A lack of consensus persists regarding the origin of photoluminescence in silicon nanocrystals. Here we report pressure-dependences of X-ray diffraction and photoluminescence from alkane-terminated colloidal particles. We determine the diamond-phase bulk modulus, observe multiple phase transitions, and importantly find a systematic photoluminescence red shift that matches the X(conduction)-to-Γ(valence) transition of bulk crystalline silicon. These results, reinforced by calculations, suggest that the efficient photoluminescence, frequently attributed to defects, arises instead from core-states that remain highly indirect despite quantum confinement.

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.

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.

First principles simulations of the electrochemical lithiation and delithiation of faceted crystalline silicon.

Silicon is of significant interest as a next-generation anode material for lithium-ion batteries due to its extremely high capacity. The reaction of lithium with crystalline silicon is known to present a rich range of phenomena, including electrochemical solid state amorphization, crystallization at full lithiation of a Li(15)Si(4) phase, hysteresis in the first lithiation-delithiation cycle, and highly anisotropic lithiation in crystalline samples. Very little is known about these processes at an atomistic level, however. To provide fundamental insights into these issues, we develop and apply a first principles, history-dependent, lithium insertion and removal algorithm to model the process of lithiation and subsequent delithiation of crystalline Si. The simulations give a realistic atomistic picture of lithiation demonstrating, for the first time, the amorphization process and hinting at the formation of the Li(15)Si(4) phase. Voltages obtained from the simulations show that lithiation of the (110) surface is thermodynamically more favorable than lithiation of the (100) or (111) surfaces, providing an explanation for the drastic lithiation anisotropy seen in experiments on Si micro- and nanostructures. Analysis of the delithiation and relithiation processes also provides insights into the underlying physics of the lithiation-delithiation hysteresis, thus providing firm conceptual foundations for future design of improved Si-based anodes for Li ion battery applications.

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.