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.

Electrolyte Coatings for High Adhesion Interfaces in Solid-State Batteries from First Principles.

We introduce an adhesion parameter that enables rapid screening for materials interfaces with high adhesion. This parameter is obtained by density functional theory calculations of individual single-material slabs rather than slabs consisting of combinations of two materials, eliminating the need to calculate all configurations of a prohibitively vast space of possible interface configurations. Cleavage energy calculations are used as an upper bound for electrolyte and coating energies and implemented in an adapted contact angle equation to derive the adhesion parameter. In addition to good adhesion, we impose further constraints in electrochemical stability window, abundance, bulk reactivity, and stability to screen for coating materials for next-generation solid-state batteries. Good adhesion is critical in combating delamination and resistance to lithium diffusivity in solid-state batteries. Here, we identify several promising coating candidates for the Li7La3Zr2O12 and sulfide electrolyte systems including the previously investigated electrode coating materials LiAlSiO4 and Li5AlO8, making them especially attractive for experimental optimization and commercialization.

Emergent layer stacking arrangements in c-axis confined MoTe2.

The layer stacking order in 2D materials strongly affects functional properties and holds promise for next-generation electronic devices. In bulk, octahedral MoTe2 possesses two stacking arrangements, the ferroelectric Weyl semimetal Td phase and the higher-order topological insulator 1T' phase. However, in thin flakes of MoTe2, it is unclear if the layer stacking follows the Td, 1T', or an alternative stacking sequence. Here, we use atomic-resolution scanning transmission electron microscopy to directly visualize the MoTe2 layer stacking. In thin flakes, we observe highly disordered stacking, with nanoscale 1T' and Td domains, as well as alternative stacking arrangements not found in the bulk. We attribute these findings to intrinsic confinement effects on the MoTe2 stacking-dependent free energy. Our results are important for the understanding of exotic physics displayed in MoTe2 flakes. More broadly, this work suggests c-axis confinement as a method to influence layer stacking in other 2D materials.

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.

Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales Using Kinetic Models: Applications to Hydrocarbon Pyrolysis.

We develop a method to construct temperature-dependent kinetic models of hydrocarbon pyrolysis, based on information from molecular dynamics (MD) simulations of pyrolyzing systems in the high-temperature regime. MD simulations are currently a key tool to understand the mechanism of complex chemical processes such as pyrolysis and to observe their outcomes in different conditions, but these simulations are computationally expensive and typically limited to nanoseconds of simulation time. This limitation is inconsequential at high temperatures, where equilibrium is reached quickly, but at low temperatures, the system may not equilibrate within a tractable simulation timescale. In this work, we develop a method to construct kinetic models of hydrocarbon pyrolysis using the information from the high-temperature high-reactivity regime. We then extrapolate this model to low temperatures, which enables microsecond-long simulations to be performed. We show that this approach accurately predicts the time evolution of small molecules, as well as the size and composition of long carbon chains across a wide range of temperatures and compositions. Further, we show that the range of suitable temperatures for extrapolation can easily be improved by adding more simulations to the training data. Compared to experimental results, our kinetic model leads to similar compositional trends while allowing for more detailed kinetic and mechanistic insights.

Automated prediction of lattice parameters from X-ray powder diffraction patterns.

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening.

The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

Highly Efficient Uniaxial In-Plane Stretching of a 2D Material via Ion Insertion.

On-chip dynamic strain engineering requires efficient micro-actuators that can generate large in-plane strains. Inorganic electrochemical actuators are unique in that they are driven by low voltages (≈1 V) and produce considerable strains (≈1%). However, actuation speed and efficiency are limited by mass transport of ions. Minimizing the number of ions required to actuate is thus key to enabling useful "straintronic" devices. Here, it is shown that the electrochemical intercalation of exceptionally few lithium ions into WTe2 causes large anisotropic in-plane strain: 5% in one in-plane direction and 0.1% in the other. This efficient stretching of the 2D WTe2 layers contrasts to intercalation-induced strains in related materials which are predominantly in the out-of-plane direction. The unusual actuation of Lix WTe2 is linked to the formation of a newly discovered crystallographic phase, referred to as Td', with an exotic atomic arrangement. On-chip low-voltage (<0.2 V) control is demonstrated over the transition to the novel phase and its composition. Within the Td'-Li0.5- δ WTe2 phase, a uniaxial in-plane strain of 1.4% is achieved with a change of δ of only 0.075. This makes the in-plane chemical expansion coefficient of Td'-Li0.5-δ WTe2 far greater than of any other single-phase material, enabling fast and efficient planar electrochemical actuation.

Spectrum of Exfoliable 1D van der Waals Molecular Wires and Their Electronic Properties.

Two-dimensional (2D) materials derived from van der Waals (vdW)-bonded layered crystals have been the subject of considerable research focus, but their one-dimensional (1D) analogues have received less attention. These bulk crystals consist of covalently bonded multiatom atomic chains with weak van der Waals bonds between adjacent chains. Using density-functional-theory-based methods, we find the binding energies of several 1D families of materials to be within typical exfoliation ranges possible for 2D materials. In addition, we compute the electronic properties of a variety of insulating, semiconducting, and metallic individual wires and find differences that could enable the identification of and distinction between 1D, 2D, and 3D forms during mechanical exfoliation onto a substrate. We find 1D wires from chemical families of the forms PdBr2, SbSeI, and GePdS3 are likely to be distinguishable from bulk materials via photoluminescence. Like 2D vdW materials, we find some of these 1D vdW materials have the potential to retain their bulk properties down to nearly atomic film thicknesses, including the structural families of HfI3 and PNF2, a useful property for some applications including electronic interconnects. We also study naturally occurring bulk crystalline heterostructures of 1D wires and identify two families that are likely to be exfoliable and identifiable as individual 1D wire subcomponents.

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.

Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium-Boron-Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes.

We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium-boron-sulfur (Li-B-S) system: 62 (+9, -2) mS cm-1 in Li5B7S13 and 80 (-56, -41) mS cm-1 in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm -1 in Li2B2S5 and between 0.0031 and 9.7 mS cm-1 in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li-B-S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li-B-S system also has a low elemental cost of approximately 0.05 USD/m2 per 10 µm thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm3. These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.

Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning.

The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence.

Reversible Electrochemical Phase Change in Monolayer to Bulk-like MoTe2 by Ionic Liquid Gating.

Transition-metal dichalcogenides (TMDs) exist in various crystal structures with semiconducting, semi-metallic, and metallic properties. The dynamic control of these phases is of immediate interest for next-generation electronics such as phase change memories. Of the binary Mo and W-based TMDs, MoTe2 is attractive for electronic applications because it has the lowest energy difference (40 meV) between the semiconducting (2H) and semi-metallic (1T') phases, allowing for MoTe2 phase change by electrostatic doping. Here, we report phase change between the 2H and 1T' polymorphs of MoTe2 in thicknesses ranging from the monolayer to bulk-like case (73 nm) using an ionic liquid electrolyte at room temperature and in air. We find consistent evidence of a partially reversible 2H-1T' transition using in situ Raman spectroscopy where the phase change occurs in the topmost layers of the MoTe2 flake. We find a thickness-dependent transition voltage where higher voltages are necessary to drive the phase change for thicker flakes. We also show evidence of electrochemical activity during the gating process by observation of Te metal formation. This finding suggests the formation of Te vacancies which have been reported to lower the energy difference between the 2H and 1T' phases, potentially aiding the phase change process. Our discovery that the phase change can be achieved on the surface layer of bulk-like materials reveals that this electrochemical mechanism does not require isolation of a single layer and the effect may be more broadly applicable than previously thought.

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.

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.

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.

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.

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.