Multi-Objective Optimization for Rapid Identification of Novel Compound Metals for Interconnect Applications.

Interconnect materials play the critical role of routing energy and information in integrated circuits. However, established bulk conductors, such as copper, perform poorly when scaled down beyond 10 nm, limiting the scalability of logic devices. Here, a multi-objective search is developed, combined with first-principles calculations, to rapidly screen over 15,000 materials and discover new interconnect candidates. This approach simultaneously optimizes the bulk electronic conductivity, surface scattering time, and chemical stability using physically motivated surrogate properties accessible from materials databases. Promising local interconnects are identified that have the potential to outperform ruthenium, the current state-of-the-art post-Cu material, and also semi-global interconnects with potentially large skin depths at the GHz operation frequency. The approach is validated on one of the identified candidates, CoPt, using both ab initio and experimental transport studies, showcasing its potential to supplant Ru and Cu for future local interconnects.

Structural phase transition in monolayer MoTe2 driven by electrostatic doping.

Monolayers of transition-metal dichalcogenides (TMDs) exhibit numerous crystal phases with distinct structures, symmetries and physical properties. Exploring the physics of transitions between these different structural phases in two dimensions may provide a means of switching material properties, with implications for potential applications. Structural phase transitions in TMDs have so far been induced by thermal or chemical means; purely electrostatic control over crystal phases through electrostatic doping was recently proposed as a theoretical possibility, but has not yet been realized. Here we report the experimental demonstration of an electrostatic-doping-driven phase transition between the hexagonal and monoclinic phases of monolayer molybdenum ditelluride (MoTe2). We find that the phase transition shows a hysteretic loop in Raman spectra, and can be reversed by increasing or decreasing the gate voltage. We also combine second-harmonic generation spectroscopy with polarization-resolved Raman spectroscopy to show that the induced monoclinic phase preserves the crystal orientation of the original hexagonal phase. Moreover, this structural phase transition occurs simultaneously across the whole sample. This electrostatic-doping control of structural phase transition opens up new possibilities for developing phase-change devices based on atomically thin membranes.

Predicting molecule size distribution in hydrocarbon pyrolysis using random graph theory.

Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in particular, for understanding the processes occurring in the interior of icy planets, such as Uranus and Neptune, where small hydrocarbons are subjected to high temperature and pressure. We propose a computationally cheap methodology based on an originally developed ten-reaction model and the configurational model from random graph theory. This methodology generates accurate predictions for molecule size distributions for a variety of initial chemical compositions and temperatures ranging from 3200 to 5000 K. Specifically, we show that the size distribution of small molecules is particularly well predicted, and the size of the largest molecule can be accurately predicted provided that this molecule is not too large.

Pit viper thermography: the pit organ used by crotaline snakes to detect thermal contrast has poor spatial resolution.

Pit vipers detect infrared radiation by means of temperature contrasts created on their pit organ membranes. Signals from pit organs integrate with visual signals in the optic tectum, leading to the conjecture that the facial pits operate as an extension of the visual system. Because similar mechanisms underlie thermal imaging technology, imagery from thermal cameras is often used to infer how pit vipers perceive their environment. However, pit organs lack a focusing mechanism, and biophysical models predict that pit organs should have poor spatial resolution compared with thermal imaging cameras. Nevertheless, behavioral studies occasionally suggest pits may have better resolution than predicted by biophysical models, indicating that processing in the central nervous system may improve imaging. To estimate the spatial resolution of the neural image informing behavior, we recorded snake responses evoked by targets moving across backgrounds composed of two contrasting temperatures with an average temperature equal to the target temperature. An unresolved background would appear uniform; thus, the target would be detectable only if the background pattern were resolved. Western rattlesnakes (Crotalus oreganus) displayed no statistically significant responses to targets presented in front of patterned backgrounds, regardless of the temperature contrasts or spatial frequencies within the background, but responded strongly to targets presented in front of homogeneous backgrounds. We found no evidence that the pit organ system can resolve spatial details subtending an angle of 9 deg or less. We discuss the implications of these results for understanding pit organ function in ecologically relevant habitats with thermal heterogeneity.

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations.

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

Valence-Dependent Electrical Conductivity in a 3D Tetrahydroxyquinone-Based Metal-Organic Framework.

Electrically conductive metal-organic frameworks (cMOFs) have become a topic of intense interest in recent years because of their great potential in electrochemical energy storage, electrocatalysis, and sensing applications. Most of the cMOFs reported hitherto are 2D structures, and 3D cMOFs remain rare. Herein we report FeTHQ, a 3D cMOF synthesized from tetrahydroxy-1,4-quinone (THQ) and iron(II) sulfate salt. FeTHQ exhibited a conductivity of 3.3 ± 0.55 mS cm-1 at 300 K, which is high for 3D cMOFs. The conductivity of FeTHQ is valence-dependent. A higher conductivity was measured with the as-prepared FeTHQ than with the air-oxidized and sodium naphthalenide-reduced samples.

Dipole-phonon quantum logic with alkaline-earth monoxide and monosulfide cations.

Dipole-phonon quantum logic (DPQL) leverages the interaction between polar molecular ions and the motional modes of a trapped-ion Coulomb crystal to provide a potentially scalable route to quantum information science. Here, we study a class of candidate molecular ions for DPQL, the cationic alkaline-earth monoxides and monosulfides, which possess suitable structure for DPQL and can be produced in existing atomic ion experiments with little additional complexity. We present calculations of DPQL operations for one of these molecules, CaO+, and discuss progress towards experimental realization. We also further develop the theory of DPQL to include state preparation and measurement and entanglement of multiple molecular ions.

New Assembly-Free Bulk Layered Inorganic Vertical Heterostructures with Infrared and Optical Bandgaps.

In principle, a nearly endless number of unique van der Waals heterostructures can be created through the vertical stacking of two-dimensional (2D) materials, resulting in unprecedented potential for material design. However, this widely employed synthetic method for generating van der Waals heterostructures is slow, imprecise, and prone to introducing interlayer contaminants when compared with synthesis methods that are scalable to industrially relevant scales. Herein, we study the properties of a new class of layered bulk inorganic materials that has recently been reported that we call assembly-free bulk layered inorganic heterostructures, wherein the individual layers are of dissimilar chemical composition, distinguishing them from commonly studied layered materials. We find that these bulk materials exhibit properties similar to vertical heterostructures but without the complex and unscalable stacking process. Using state-of-the-art computational approaches, we study the electronic properties of livingstonite (HgSb4S8), a naturally occurring mineral that is a bulk lattice-commensurate heterostructure. We find that isolated bilayers of livingstonite have an intralayer HSE-06 band gap of 2.08 eV. This is the first report of a naturally occurring van der Waals heterostructure with a calculated band gap in the visible spectrum. We also studied the electronic properties of tetragonal Ti3Bi4O12, Sm2Ti3Bi2O12, orthorhombic Ti3Bi4O12, Nb3Bi5O15, LaTiNbBi2O9, and AgPbBrO and found some of them are potentially well-suited for photovoltaic applications. We also provide characterization of the electronic structure of the isolated bilayer and monolayer subcomponents of the bulk heterostructures. The report of the properties of these materials significantly enhances the library of known van der Waals heterostructures.

Low Resistivity and High Breakdown Current Density of 10 nm Diameter van der Waals TaSe3 Nanowires by Chemical Vapor Deposition.

Micron-scale single-crystal nanowires of metallic TaSe3, a material that forms -Ta-Se3-Ta-Se3- stacks separated from one another by a tubular van der Waals (vdW) gap, have been synthesized using chemical vapor deposition (CVD) on a SiO2/Si substrate, in a process compatible with semiconductor industry requirements. Their electrical resistivity was found unaffected by downscaling from the bulk to as little as 7 nm in nanowire width and height, in striking contrast to the resistivity of copper for the same dimensions. While the bulk resistivity of TaSe3 is substantially higher than that of bulk copper, at the nanometer scale the TaSe3 wires become competitive to similar-sized copper ones. Moreover, we find that the vdW TaSe3 nanowires sustain current densities in excess of 108 A/cm2 and feature an electromigration energy barrier twice that of copper. The results highlight the promise of quasi-one-dimensional transition metal trichalcogenides for electronic interconnect applications and the potential of van der Waals materials for downscaled electronics.

Variable domain N-linked glycosylation and negative surface charge are key features of monoclonal ACPA: Implications for B-cell selection.

Autoreactive B cells have a central role in the pathogenesis of rheumatoid arthritis (RA), and recent findings have proposed that anti-citrullinated protein autoantibodies (ACPA) may be directly pathogenic. Herein, we demonstrate the frequency of variable-region glycosylation in single-cell cloned mAbs. A total of 14 ACPA mAbs were evaluated for predicted N-linked glycosylation motifs in silico, and compared to 452 highly-mutated mAbs from RA patients and controls. Variable region N-linked motifs (N-X-S/T) were strikingly prevalent within ACPA (100%) compared to somatically hypermutated (SHM) RA bone marrow plasma cells (21%), and synovial plasma cells from seropositive (39%) and seronegative RA (7%). When normalized for SHM, ACPA still had significantly higher frequency of N-linked motifs compared to all studied mAbs including highly mutated HIV broadly-neutralizing and malaria-associated mAbs. The Fab glycans of ACPA-mAbs were highly sialylated, contributed to altered charge, but did not influence antigen binding. The analysis revealed evidence of unusual B-cell selection pressure and SHM-mediated decrease in surface charge and isoelectric point in ACPA. It is still unknown how these distinct features of anti-citrulline immunity may have an impact on pathogenesis. However, it is evident that they offer selective advantages for ACPA+ B cells, possibly through non-antigen driven mechanisms.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data.

Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding.

ODE integration schemes for plane-wave real-time time-dependent density functional theory.

Integration schemes are implemented with a plane-wave basis in the context of real-time time-dependent density functional theory. Crank-Nicolson methods and three classes of explicit integration schemes are explored and assessed in terms of their accuracy and stability properties. Within the framework of plane-wave density functional theory, a graphene monolayer system is used to investigate the error, stability, and serial computational cost of these methods. The results indicate that Adams-Bashforth and Adams-Bashforth-Moulton methods of orders 4 and 5 outperform commonly used methods, including Crank-Nicolson and Runge-Kutta methods, in simulations where a relatively low error is desired. Parallel runtime scaling of the most competitive serial methods is presented, further demonstrating that the Adams-Bashforth and Adams-Bashforth-Moulton methods are efficient methods for propagating the time-dependent Kohn-Sham equations. Our integration schemes are implemented as an extension to the Quantum ESPRESSO code.

Electrically Conductive Copper Core-Shell Nanowires through Benzenethiol-Directed Assembly.

Ultrathin nanowires with <3 nm diameter have long been sought for novel properties that emerge from dimensional constraint as well as for continued size reduction and performance improvement of nanoelectronic devices. Here, we report on a facile and large-scale synthesis of a new class of electrically conductive ultrathin core-shell nanowires using benzenethiols. Core-shell nanowires are atomically precise and have inorganic five-atom copper-sulfur cross-sectional cores encapsulated by organic shells encompassing aromatic substituents with ring planes oriented parallel. The exact nanowire atomic structures were revealed via a two-pronged approach combining computational methods coupled with experimental synthesis and advanced characterizations. Core-shell nanowires were determined to be indirect bandgap materials with a predicted room-temperature resistivity of ∼120 Ω·m. Nanowire morphology was found to be tunable by changing the interwire interactions imparted by the functional group on the benzenethiol molecular precursors, and the nanowire core diameter was determined by the steric bulkiness of the ligand. These discoveries help define our understanding of the fundamental constituents of atomically well-defined and electrically conductive core-shell nanowires, representing significant advances toward nanowire building blocks for smaller, faster, and more powerful nanoelectronics.

Anticitrullinated protein/peptide antibody multiplexing defines an extended group of ACPA-positive rheumatoid arthritis patients with distinct genetic and environmental determinants.

INTRODUCTION: The second generation anticycliccitrullinated peptide (anti-CCP2) assay detects the majority but not all anticitrullinated protein/peptide antibodies (ACPA). Anti-CCP2-positive rheumatoid arthritis (RA) is associated with HLA-DRB1* shared epitope (SE) alleles and smoking. Using a multiplex assay to detect multiple specific ACPA, we have investigated the fine specificity of individual ACPA responses and the biological impact of additional ACPA reactivity among anti-CCP2-negative patients. METHODS: We investigated 2825 patients with RA and 551 healthy controls with full data on anti-CCP2, HLA-DRB1* alleles and smoking history concerning reactivity against 16 citrullinated peptides and arginine control peptides with a multiplex array. RESULTS: The prevalence of the 16 ACPA specificities ranged from 9% to 58%. When reactivity to arginine peptides was subtracted, the mean diagnostic sensitivity increased by 3.2% with maintained 98% specificity. Of the anti-CCP2-negative patients, 16% were found to be ACPA positive. All ACPA specificities associated with SE, and all but one with smoking. Correction for arginine reactivity also conveyed a stronger association with SE for 13/16 peptides. Importantly, when all ACPA specificities were analysed together, SE and smoking associated with RA in synergy among ACPA positive, but not among ACPA-negative subjects also in the anti-CCP2-negative subset. CONCLUSIONS: Multiplexing detects an enlarged group of ACPA-positive but anti-CCP2-negative patients with genetic and environmental attributes previously assigned to anti-CCP2-positive patients. The individual correction for arginine peptide reactivity confers both higher diagnostic sensitivity and stronger association to SE than gross ACPA measurement.

Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures.

Layered materials held together by weak interactions including van der Waals forces, such as graphite, have attracted interest for both technological applications and fundamental physics in their layered form and as an isolated single-layer. Only a few dozen single-layer van der Waals solids have been subject to considerable research focus, although there are likely to be many more that could have superior properties. To identify a broad spectrum of layered materials, we present a novel data mining algorithm that determines the dimensionality of weakly bonded subcomponents based on the atomic positions of bulk, three-dimensional crystal structures. By applying this algorithm to the Materials Project database of over 50,000 inorganic crystals, we identify 1173 two-dimensional layered materials and 487 materials that consist of weakly bonded one-dimensional molecular chains. This is an order of magnitude increase in the number of identified materials with most materials not known as two- or one-dimensional materials. Moreover, we discover 98 weakly bonded heterostructures of two-dimensional and one-dimensional subcomponents that are found within bulk materials, opening new possibilities for much-studied assembly of van der Waals heterostructures. Chemical families of materials, band gaps, and point groups for the materials identified in this work are presented. Point group and piezoelectricity in layered materials are also evaluated in single-layer forms. Three hundred and twenty-five of these materials are expected to have piezoelectric monolayers with a variety of forms of the piezoelectric tensor. This work significantly extends the scope of potential low-dimensional weakly bonded solids to be investigated.

Dynamic Optical Tuning of Interlayer Interactions in the Transition Metal Dichalcogenides.

Modulation of weak interlayer interactions between quasi-two-dimensional atomic planes in the transition metal dichalcogenides (TMDCs) provides avenues for tuning their functional properties. Here we show that above-gap optical excitation in the TMDCs leads to an unexpected large-amplitude, ultrafast compressive force between the two-dimensional layers, as probed by in situ measurements of the atomic layer spacing at femtosecond time resolution. We show that this compressive response arises from a dynamic modulation of the interlayer van der Waals interaction and that this represents the dominant light-induced stress at low excitation densities. A simple analytic model predicts the magnitude and carrier density dependence of the measured strains. This work establishes a new method for dynamic, nonequilibrium tuning of correlation-driven dispersive interactions and of the optomechanical functionality of TMDC quasi-two-dimensional materials.

Nanosecond homogeneous nucleation and crystal growth in shock-compressed SiO2.

Understanding the kinetics of shock-compressed SiO2 is of great importance for mitigating optical damage for high-intensity lasers and for understanding meteoroid impacts. Experimental work has placed some thermodynamic bounds on the formation of high-pressure phases of this material, but the formation kinetics and underlying microscopic mechanisms are yet to be elucidated. Here, by employing multiscale molecular dynamics studies of shock-compressed fused silica and quartz, we find that silica transforms into a poor glass former that subsequently exhibits ultrafast crystallization within a few nanoseconds. We also find that, as a result of the formation of such an intermediate disordered phase, the transition between silica polymorphs obeys a homogeneous reconstructive nucleation and grain growth model. Moreover, we construct a quantitative model of nucleation and grain growth, and compare its predictions with stishovite grain sizes observed in laser-induced damage and meteoroid impact events.

Dynamic Structural Response and Deformations of Monolayer MoS2 Visualized by Femtosecond Electron Diffraction.

Two-dimensional materials are subject to intrinsic and dynamic rippling that modulates their optoelectronic and electromechanical properties. Here, we directly visualize the dynamics of these processes within monolayer transition metal dichalcogenide MoS2 using femtosecond electron scattering techniques as a real-time probe with atomic-scale resolution. We show that optical excitation induces large-amplitude in-plane displacements and ultrafast wrinkling of the monolayer on nanometer length-scales, developing on picosecond time-scales. These deformations are associated with several percent peak strains that are fully reversible over tens of millions of cycles. Direct measurements of electron-phonon coupling times and the subsequent interfacial thermal heat flow between the monolayer and substrate are also obtained. These measurements, coupled with first-principles modeling, provide a new understanding of the dynamic structural processes that underlie the functionality of two-dimensional materials and open up new opportunities for ultrafast strain engineering using all-optical methods.