Tool For Material Design Research Articles

Chemical state analysis of reactively sputtered zinc vanadium nitride: The Auger parameter as a tool in materials design

Photoelectron spectroscopy is an important tool for the development of new materials. However, especially for nitride semiconductors, the formation of surface oxides, surface band bending as well as the lack of a suitable charge reference often prevent a robust analysis. Here, we perform a comprehensive chemical state analysis of the Zn-V-N phase space using the Auger parameter concept, which is less sensitive to such uncertainties. Phase-pure Zn2VN3, VN, and Zn3N2 samples are analyzed in a dual-source lab-based HAXPES system, after transfer in inert-gas atmosphere. XPS and HAXPES measurements are performed using Al kα and Cr kα, respectively. In addition, high-throughput chemical state analysis is performed on combinatorial Zn1-xVxN thin film libraries using conventional XPS. The evolution of the Zn Auger parameter in Zn1-xVxN is consistent with previous mapping of the structural and functional properties. Strikingly, the study reveals a narrower stability range of wurtzite Zn1-xVxN than our previous high-throughput XRD screening, highlighting the sensitivity of the measurement approach. The procedures applied here are transferable to many other material systems and could be particularly useful for the high-throughput development of materials with low crystallinity where insights from XRD screenings are limited.

Electrochemical dealloying in a magnetic field – Tapping the potential for catalyst and material design

Nanocatalyst optimisation through electrochemical dealloying has been employed as a successful strategy to increase catalytic activity, while reducing the need for precious metals. We present here a new pathway to influence the electrochemical dealloying, through external homogeneous magnetic fields. A homogeneous magnetic field with a flux density of 450 mT in two orientations, parallel or perpendicular to the current direction, was used during electrochemical dealloying using cyclic voltammetry of AgAu nanoparticles. We found increased porosity for low dealloying cycle numbers and improved catalytic properties after longer cycling, compared to nanoparticles dealloyed in the absence of magnetic fields. These findings demonstrate that magnetic fields applied during electrochemical dealloying have currently untapped potential that can be used to influence material properties in a new way and give researchers another powerful tool for material design.

Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small-Scale Dataset.

Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.

Meso-scale Finite Element (FE) modelling of biaxial carbon fibre non-crimp-fabric (NCF) based composites under uniaxial tension and in-plane shear

Non-crimp-fabrics (NCF) are promising materials in aerospace applications. The complex internal structure of NCF composites could influence the in-plane performances, which needs to be comprehensively studied. The novel three-dimensional (3D) meso-scale repeated unit cell (RUC) models were proposed for biaxial NCF composites based on the Finite Element (FE) method to conduct a systematic parameter study, including layup sequence, out-of-plane tow waviness, resin-rich areas, transverse tow placements and delamination. The meso RUC model could effectively predict the homogenised uniaxial tensile and in-plane shear properties of biaxial NCF composites based on their meso-scale constituent and material properties. A multiscale framework was also developed for biaxial NCF composites. A micromechanical representative volume element (RVE) model provided homogenised mechanical properties for tows, and a macroscopical FE model validated the test results using the homogenised results obtained from meso RUC models. The numerical results were in good agreement with the experiment results. Therefore, the multiscale framework provides an insight into the critical parameters influencing the in-plane properties of NCF composites and an analysis tool for NCF material design.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

A Multilevel Analytical Theory for Prediction of Ferroelectric Perovskite Oxide Properties from Composition.

Prediction of properties from composition is a fundamental goal of materials science that is particularly relevant for ferroelectric perovskite oxide solid solutions where compositional variation is a primary tool for material design. Design of ferroelectric oxide solid solutions has been guided by heuristics and first-principles and Landau-Ginzburg-Devonshire theoretical methods that become increasingly difficult to apply in ternary, quaternary, and quintary solid solutions. To address this problem, a multilevel model is developed for the prediction of the ferroelectric-to-paraelectric transition temperature (Tc ), coercive field (Ec ), and polarization (P) of PbTiO3 -derived ferroelectric solid solutions from composition. The characteristics of the materials at different length scales, starting at the level of the electronic structure and chemical bonding of the constituent ions and ending at the level of collective behavior, are analytically related by using ferroelectric domain walls and cationic off-center displacements as the key links between the different levels of the model. The obtained composition-structure-property relationships provide a unified quantitatively predictive theory for understanding PbTiO3 -derived solid solutions. Such a multilevel analytical modeling approach is likely to be generally applicable to different classes of ferroelectric perovskite oxides and to other functional properties, and to materials and properties beyond the field of ferroelectrics.

Research and applications of artificial neural network in pavement engineering: A state-of-the-art review

Given the great advancements in soft computing and data science, artificial neural network (ANN) has been explored and applied to handle complicated problems in the field of pavement engineering. This study conducted a state-of-the-art review for surveying the recent progress of ANN application at different stages of pavement engineering, including pavement design, construction, inspection and monitoring, and maintenance. This study focused on the papers published over the last three decades, especially the studies conducted since 2013. Through literature retrieval, a total of 683 papers in this field were identified, among which 143 papers were selected for an in-depth review. The ANN architectures used in these studies mainly included multi-layer perceptron neural network (MLPNN), convolutional neural network (CNN) and recurrent neural network (RNN) for processing one-dimensional data, two-dimensional data and time-series data. CNN-based pavement health inspection and monitoring attracted the largest research interest due to its potential to replace human labor. While ANN has been proved to be an effective tool for pavement material design, cost analysis, defect detection and maintenance planning, it is facing huge challenges in terms of data collection, parameter optimization, model transferability and low-cost data annotation. More attention should be paid to bring multidisciplinary techniques into pavement engineering to tackle existing challenges and widen future opportunities. • Frontiers of artificial neural network (ANN) in pavement design, construction, inspection and maintenance were reviewed. • Three mainstream ANN architectures for different application scenarios were summarized. • Five research challenges and prospects of ANN application in pavement engineering were analyzed. • Standardized literature search and classification methods were implemented.

Modeling of the overcharge behavior of lithium-ion battery cells protected by a voltage-switchable resistive polymer layer

Recently, a novel approach to overcharge protection of Li-ion batteries by voltage-switchable resistive polymer layer, placed between the cathode active mass and the current collector, was reported. The unique feature of the layer under consideration is the ability to increase the electrode resistance when the cathode potential exceeds operating limits, preventing cell overcharge and degradation. Here we extend the full-order Newman model of a lithium-ion battery cell by adding a description of the novel type of overcharge protection layer. The model was used to simulate Constant Current, Constant Voltage, and Constant Power charging modes as well as thermal effects and was implemented in Multiphysics (MP) software (COMSOL Inc., Palo Alto, CA). Comparison of electrochemical responses in the simulated modes demonstrated that the cell protected via the protective layer has advantages when operating at any mode. Therefore, the computational study proves the effectiveness of the usage of the protective layer with switchable resistance. Moreover, this model is a powerful tool for material design and engineering tasks in LIB development as it allows fast and simple estimation of resistance and other parameters’ values that meet a given safety standard.

Theoretical advances in two-dimensional materials for photocatalytic water splitting

<p indent=0mm>Currently, the energy and environment associated problems become more and more serious. Hydrogen is a kind of clean and renewable resource. Hydrogen generation through photocatalytic water splitting is a feasible and efficient route to resolve these problems. In general, the photocatalytic water splitting process is composed of three pivotal steps: Light harvest, carrier separation and transportation, and hydrogen evolution reaction (HER) as well as oxygen evolution reaction (OER). Improving the efficiencies of these three steps are keys to enhance the solar-to-hydrogen (STH) efficiency, which stimulates the development of high-performance photocatalysts. Typically, for two-dimensional (2D) materials, the photo-generated electrons and holes produce directly from the surfaces of 2D materials with atomic thickness. In addition, compared with their bulk counterparts, 2D materials with an extremely large specific surface area, rather high carrier mobility, and controllable interfaces offer a large number of advantages for the photocatalysts exploration. Thus, search and design of 2D photocatalysts for water splitting has been a hot topic. Density functional theory (DFT) based first-principles calculations is a powerful tool for materials design and has been widely used in exploring 2D photocatalysts for water splitting. Here, recent advances in the design of 2D materials for photocatalytic water splitting are presented from a theoretical perspective. In the initial stage, a great of new 2D photocatalysts for water splitting are expected to be discovered. Mechanical and chemical assisted exfoliation of layered materials is straightforward approaches to get access to single-layer and few-layer 2D materials. By using DFT calculations, many potential 2D photocatalysts are screened out for water splitting. In addition, molecular designing and chemical regulating are alternative approaches for exploring new 2D photocatalysts for water splitting. The light harvest is the initial process of photocatalytic water splitting, and increasing the light adsorption is one of keys to enhance the STH efficiency. Generally, there are two ways for enhancing the light harvest of 2D materials. One strategy is designing and constructing new 2D materials for photocatalytic water splitting. The other one is improving the light harvest by regulating 2D materials that have been reported previously. Both of the two strategies are employed for developing 2D visible-light-driven covalent organic frameworks for photocatalytic water splitting. Improving the separation of the photo-generated electrons and holes is another key to enhance the STH efficiency. The development of (type-II and Z-scheme) heterojunctions provides a feasible way to design high performance photocatalysts for water splitting due to the benefit for inhibiting electron-hole recombination. That is because that the HER and OER occur on different materials. By using first-principles calculations, a variety of 2D materials based heterojunctions with an expectation of designing highly efficient photocatalytic 2D materials for water splitting have been developed. The STH efficiency is determined by the product of the efficiencies of all the steps for photocatalytic water splitting. Thus, the successful enhancement of energy conversion efficiency calls for the simultaneous improvement for the light harvest and electron-hole separation. A new mechanism for photocatalytic water splitting reveals that the vertical intrinsic electric field in 2D material not only accelerates the carrier separation, but also breaks the conventional limitation of <sc>1.23 eV</sc> for band gap of photocatalysts, leading to enlarged light absorption, even to infrared region. Referring to this new mechanism, some 2D materials with vertical electric field have been theoretically proved to be potential photocatalysts for water splitting. In additional, future opportunities and challenges in theoretical design of 2D photocatalysts toward water splitting are also included.

Graphene gets bent

Graphene gets bent

Green light triggered [2+2] cycloaddition of halochromic styrylquinoxaline\u2014controlling photoreactivity by pH

Photochemical reactions are a powerful tool in (bio)materials design due to the spatial and temporal control light can provide. To extend their applications in biological setting, the use of low-energy, long wavelength light with high penetration propertiesis required. Further regulation of the photochemical process by additional stimuli, such as pH, will open the door for construction of highly regulated systems in nanotechnology- and biology-driven applications. Here we report the green light induced [2+2] cycloaddition of a halochromic system based on a styrylquinoxaline moiety, which allows for its photo-reactivity to be switched on and off by adjusting the pH of the system. Critically, the [2+2] photocycloaddition can be activated by green light (λ up to 550 nm), which is the longest wavelength employed to date in catalyst-free photocycloadditions in solution. Importantly, the pH-dependence of the photo-reactivity was mapped by constant photon action plots. The action plots further indicate that the choice of solvent strongly impacts the system’s photo-reactivity. Indeed, higher conversion and longer activation wavelengths were observed in water compared to acetonitrile under identical reaction conditions. The wider applicability of the system was demonstrated in the crosslinking of an 8-arm PEG to form hydrogels (ca. 1 cm in thickness) with a range of mechanical properties and pH responsiveness, highlighting the potential of the system in materials science.

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce su...

Database of Two-Dimensional Hybrid Perovskite Materials: Open-Access Collection of Crystal Structures, Band Gaps, and Atomic Partial Charges Predicted by Machine Learning

We describe a first open-access database of experimentally investigated hybrid organic-inorganic materials with two-dimensional (2D) perovskite-like crystal structure. The database includes 515 compounds, containing 180 different organic cations, 10 metals (Pb, Sn, Bi, Cd, Cu, Fe, Ge, Mn, Pd, Sb) and 3 halogens (I, Br, Cl) known so far and will be regularly updated. The database contains a geometrical and crystal chemical analysis of the structures, which are useful to reveal quantitative structure-property relationships for this class of compounds. We show that the penetration depth of spacer organic cation into the inorganic layer and M-X-M bond angles increase in the number of inorganic layers (n). The machine learning model is developed and trained on the database, for the prediction of a band gap with accuracy within 0.1 eV. Another machine learning model is trained for the prediction of atomic partial charges with accuracy within 0.01 e. We show that the predicted values of band gaps decrease with an increase of the n and with an increase of M-X-M angles for single-layered perovskites. In general, the proposed database and machine learning models are shown to be useful tools for the rational design of new 2D hybrid perovskite materials.

Kinetic prediction of reverse intersystem crossing in organic donor\u2013acceptor molecules

Reverse intersystem crossing (RISC), the uphill spin-flip process from a triplet to a singlet excited state, plays a key role in a wide range of photochemical applications. Understanding and predicting the kinetics of such processes in vastly different molecular structures would facilitate the rational material design. Here, we demonstrate a theoretical expression that successfully reproduces experimental RISC rate constants ranging over five orders of magnitude in twenty different molecules. We show that the spin flip occurs across the singlet–triplet crossing seam involving a higher-lying triplet excited state where the semi-classical Marcus parabola is no longer valid. The present model explains the counterintuitive substitution effects of bromine on the RISC rate constants of previously unknown molecules, providing a predictive tool for material design.

Small data materials design with machine learning: When the average model knows best

Machine learning is quickly becoming an important tool in modern materials design. Where many of its successes are rooted in huge datasets, the most common applications in academic and industrial materials design deal with datasets of at best a few tens of data points. Harnessing the power of machine learning in this context is, therefore, of considerable importance. In this work, we investigate the intricacies introduced by these small datasets. We show that individual data points introduce a significant chance factor in both model training and quality measurement. This chance factor can be mitigated by the introduction of an ensemble-averaged model. This model presents the highest accuracy, while at the same time, it is robust with regard to changing the dataset size. Furthermore, as only a single model instance needs to be stored and evaluated, it provides a highly efficient model for prediction purposes, ideally suited for the practical materials scientist.

Bioinspired Conformal Transformation Acoustics

Transformation theory is a powerful tool for material design to control waves in electromagnetics, acoustics, and elastic mechanics. Deformable materials are demonstrated to guide the propagation of ultrasonic waves freely. Toothed whales, such as dolphins and porpoises, may manipulate directional beams by simply deforming their foreheads. However, existing materials face great challenges in realizing their bioacoustic function. Here, we propose a bioinspired semianalytical conformal acoustics that can predict the evolution of acoustic functions strictly. Based on this method, we design a series of acoustic steering and collimation models. The sound-speed distribution functions can be determined by conformal mappings, and the acoustic-beam steering and expansion are numerically confirmed. We further experimentally fabricate acoustic steering and collimation devices by using metamaterials. The experimental results show good agreement with theoretical predictions. The proposed bioinspired conformal acoustics may bridge the gap between an animal's biosonar and artificial materials, which show potential application value in underwater acoustics, medical ultrasonography, and other related applications.

Prediction of micrometeoroid damage to lunar construction materials using numerical modeling of hypervelocity impact events

The use of Lunar regolith for the creation of construction materials to build habitats and other infrastructure required for a Lunar base is an example of in situ resource utilization (ISRU), an important strategy for minimizing the launch mass associated with a NASA mission to the Moon. One class of solidified regolith, Biopolymer-bound Soil Composites (BSC), consists of regolith mixed with a small amount of biopolymer binding agent (10% w/w). This paper characterizes BSC's micrometeoroid impact performance using experimental and numerical methods. Micrometeoroids are a notable hazard of the Lunar environment and pose a challenging design consideration. A total of 17 hypervelocity impact experiments were conducted on BSC targets at NASA's White Sands Testing Facility. Numerical simulations of the hypervelocity impact experiments were carried out using CTH, a shock physics code developed by Sandia National Laboratories. Comparisons between the experimental craters and the simulation results indicate that there is good agreement between crater dimensions of the hypervelocity impact experiments and the CTH model. The CTH model developed in this paper provides (1) a damage prediction tool that allows for the necessary extrapolation of micrometeoroid impact velocities beyond what is experimentally achievable and into the velocity regime that is relevant for micrometeoroids and (2) a material design tool that is capable of varying material parameters computationally, ultimately allowing for the engineering and optimization of BSC's performance under impact loading.

The van de Hulst approximation for light scattering and its use for transmittance predictions in transparent ceramics

The in-line transmittances of transparent spinel and YAG ceramics with a low volume fraction of monosized spherical pores calculated via the exact Mie solution and the so-called van de Hulst approximation are compared. It is shown that the transmittances calculated via the van de Hulst approximation are in surprisingly good qualitative agreement with the exact Mie solutions. In particular, all relevant trends and features of transmittance-versus-wavelength-and-pore-size plots are correctly predicted, so that the van de Hulst approximation may be considered as a perfect tool for material design and process optimization purposes. It avoids the “unphysical” results that can occur in the Rayleigh and Fraunhofer approximations for pores sizes beyond their validity and is much simpler than the Rayleigh-Gans-Debye approximation. Tables listed in the Appendix to this paper allow the reader reconstruct the exact Mie solution results from the easily calculated van de Hulst approximation, if required.

Machine Learning for Corrosion Resistance Alloys

Determining the ability of corrosion resistance of alloys is of great importance to many applications. While one could call the pitting resistance equivalence number (PREN) a quantitative description of the relative corrosion resistance of stainless steels, it is an empirical value based on the chemical composition of the elements in alloys only, derived by mathematically fitting of experimental alloy data. It provides no scientific insight. In this work we collect a large number of experimental data published in the literature on corrosion of alloys to study the factors that determine the corrosion resistance of materials. A database that includes the alloy composition, electrochemical parameters as well as polarization testing parameters was therefore generated. Machine learning approaches (Lasso and Ridge regression) were used to find the unexpected correlations between aforementioned experimental parameters and the materials' properties. We further use the machine learning to prioritize the features that determine the corrosion resistance and anticipate that this work can be an useful tool for materials design.

Supramolecular synthons orientation rule in the crystals of benzodiazepines, quinoxalines and benzimidazoles as a predictive tool in materials design

Supramolecular synthons orientation rule in the crystals of benzodiazepines, quinoxalines and benzimidazoles as a predictive tool in materials design