Composition Space Research Articles

Molecular Engineering of Metalloporphyrins for High-Performance Energy Storage: Central Metal Matters.

Porphyrin derivatives represent an emerging class of redox-active materials for sustainable electrochemical energy storage. However, their structure-performance relationship is poorly understood, which confines their rational design and thus limits access to their full potential. To gain such understanding, we here focus on the role of the metal ion within porphyrin molecules. The A2 B2 -type porphyrin 5,15-bis(ethynyl)-10,20-diphenylporphyrin and its first-row transition metal complexes from Co to Zn are used as models to investigate the relationships between structure and electrochemical performance. It turned out that the choice of central metal atom has a profound influence on the practical voltage window and discharge capacity. The results of DFT calculations suggest that the choice of central metal atom triggers the degree of planarity of the porphyrin. Single crystal diffraction studies illustrate the consequences on the intramolecular rearrangement and packing of metalloporphyrins. Besides the direct effect of the metal choice on the undesired solubility, efficient packing and crystallinity are found to dictate the rate capability and the ion diffusion along with the porosity. Such findings open up a vast space of compositions and morphologies to accelerate the practical application of resource-friendly cathode materials to satisfy the rapidly increasing need for efficient electrical energy storage.

X‐ray absorption fine structure characterization of a multicomponent spinel catalyst

AbstractDemand for high‐performing, inexpensive catalysts has motivated the development of mixed 3d transition metal architectures. Here, x‐ray spectroscopies are uniquely positioned to inform intensive searches of compositional spaces by elucidating structure–function relationships. However, thorough analyses of complex systems are often non‐trivial. In this work, one such example was pursued, a chemical series of spinel‐based emissions catalysts of the general formula Mn0.1CuxCo2.9−xO4 for 0.1 ≤ x ≤ 0.8. The local electronic and atomic structures of these materials were characterized by x‐ray absorption near edge structure (XANES) and extended x‐ray absorption fine structure (EXAFS) analyses, respectively. EXAFS analyses required models to be constructed that captured the relevant structural changes across the chemical series. Building these models required insights from XANES, x‐ray diffraction (XRD), and inductively coupled plasma atomic emission spectroscopy analyses. XANES measurements at the Co K‐edge, when complemented with ab initio multiple scattering calculations, suggested that the fraction of Co atoms in the octahedral sites of the spinel increased as the concentration of substituted Cu increased. Similarly, XANES measurements at the Mn K‐edge suggested that the occupancy of the first coordination shell varied throughout the series. Finally, the XRD results revealed an impurity, a CuO phase, formed at higher copper concentrations. This is consistent with the results of the principal component analysis performed on the Cu K‐edge XANES spectra. These hypotheses were incorporated into the EXAFS fitting models and are consistent with the subsequent quantitative analyses.

Sn-modified BaTiO3 thin film with enhanced polarization

Hybrid molecular beam epitaxy (MBE) growth of Sn-modified BaTiO3 films was realized with varying domain structures and crystal symmetries across the entire composition space. Macroscopic and microscopic structures and the crystal symmetry of these thin films were determined using a combination of optical second harmonic generation (SHG) polarimetry and scanning transmission electron microscopy (STEM). SHG polarimetry revealed a variation in the global crystal symmetry of the films from tetragonal (P4mm) to cubic (Pm3¯m) across the composition range, x = 0 to 1 in BaTi1−xSnxO3 (BTSO). STEM imaging shows that the long-range polar order observed when the Sn content is low (x = 0.09) transformed to a short-range polar order as the Sn content increased (x = 0.48). Consistent with atomic displacement measurements from STEM, the largest polarization was obtained at the lowest Sn content of x = 0.09 in Sn-modified BaTiO3 as determined by SHG. These results agree with recent bulk ceramic reports and further identify this material system as a potential replacement for Pb-containing relaxor-based thin film devices.

Monitoring Urban Expansion by Coupling Multi-Temporal Active Remote Sensing and Landscape Analysis: Changes in the Metropolitan Area of Cordoba (Argentina) from 2010 to 2021

Uncontrolled and unsustainable urban sprawl are altering the Earth’s surface at unprecedented rates. This research explores the potential of active remote sensors for mapping urban areas, for monitoring urban expansion processes and for depicting landscape pattern dynamics in a metropolis of South America. Based on multi-temporal urban cover maps of Cordoba, Argentina, purposely derived from COSMO-SkyMed SAR data by urban extraction algorithms, we quantified urban surface increase and described urbanization processes that occurred during 2010–2021 in sectors with different degrees of soil sealing. We extracted urban extent in four time-steps using an Urban EXTent extraction (UEXT) algorithm and quantified urban expansion, identifying newly built areas on 2.5 ha cells. For these cells, we computed urban cover and a set of landscape pattern indices (PIs), and by projecting them in a composition vs. configuration Cartesian space we performed a trajectory analysis. SAR-based urban extraction and cover change proved to be very accurate. Overall accuracy and Cohen’s Kappa statistic evidenced very high values, always above 91.58% and 0.82, respectively, for urban extraction, and also above 90.50% and 0.72 concerning the accuracy of urban expansion. Cordoba’s urban surface significantly increased (≈900 ha in 10 years) following three main spatial processes in different city sectors (e.g., edge-expansion and outlying on peri-urban areas, and infill inside the ring road), which may have contrasting effects on the sustainability of the metropolitan area. Trajectory analysis highlighted non-linear relations between the urban cover and the PIs. Areas with very low and low urban intensity underwent a steep rise of both urban cover and PI values (e.g., urban patch dimension, complexity and number), depicting urban edge-expansion and outlying processes. In the areas with medium and high urban intensity the increase in patch dimension, along with the decrease in patch number and complexity, evidence the coalescence of urban areas that incorporate in the urban fabric the remnants of non-built up zones and fill the few residual green spaces. The proposed SAR mapping procedure coupled with landscape analysis proved to be useful to detect and depict different moments of urban expansion and, pending more tests on other cities and geographical conditions, it could be postulated among the RS indicators to monitor the achievement of the Sustainable Development Goals established by the United Nations.

Microscale Combinatorial Libraries for the Discovery of High-Entropy Materials.

Polyelemental material systems, specifically high-entropy alloys, promise unprecedented properties. Due to almost unlimited combinatorial possibilities, their exploration and exploitation is hard. This challenge is addressed by co-sputtering combined with shadow masking to produce a multitude of microscale combinatorial libraries in one deposition process. These thin-film composition spreads on the microscale cover unprecedented compositional ranges of high-entropy alloy systems and enable high-throughput characterization of thousands of compositions for electrocatalytic energy conversion reactions using nanoscale scanning electrochemical cell microscopy. The exemplary exploration of the composition space of two high-entropy alloy systems provides electrocatalytic activity maps for hydrogen evolution and oxygen evolution as well as oxygen reduction reactions. Activity optima in the system Ru-Rh-Pd-Ir-Pt are identified, and active noble-metal lean compositions in the system Co-Ni-Mo-Pd-Pt are discovered. This illustrates that the proposed microlibraries are a holistic discovery platform to master the multidimensionality challenge of polyelemental systems.

Automation of liquid crystal phase analysis for SAXS, including the rapid production of novel phase diagrams for SDS-water-PIL systems.

Lyotropic liquid crystal phases (LCPs) are widely studied for diverse applications, including protein crystallization and drug delivery. The structure and properties of LCPs vary widely depending on the composition, concentration, temperature, pH, and pressure. High-throughput structural characterization approaches, such as small-angle x-ray scattering (SAXS), are important to cover meaningfully large compositional spaces. However, high-throughput LCP phase analysis for SAXS data is currently lacking, particularly for patterns of multiphase mixtures. In this paper, we develop semi-automated software for high throughput LCP phase identification from SAXS data. We validate the accuracy and time-savings of this software on a total of 668 SAXS patterns for the LCPs of the amphiphile hexadecyltrimethylammonium bromide (CTAB) in 53 acidic or basic ionic liquid derived solvents, within a temperature range of 25-75 °C. The solvents were derived from stoichiometric ethylammonium nitrate (EAN) or ethanolammonium nitrate (EtAN) by adding water to vary the ionicity, and adding precursor ions of ethylamine, ethanolamine, and nitric acid to vary the pH. The thermal stability ranges and lattice parameters for CTAB-based LCPs obtained from the semi-automated analysis showed equivalent accuracy to manual analysis, the results of which were previously published. A time comparison of 40 CTAB systems demonstrated that the automated phase identification procedure was more than 20 times faster than manual analysis. Moreover, the high throughput identification procedure was also applied to 300 unpublished scattering patterns of sodium dodecyl-sulfate in the same EAN and EtAN based solvents in this study, to construct phase diagrams that exhibit phase transitions from micellar, to hexagonal, cubic, and lamellar LCPs. The accuracy and significantly low analysis time of the high throughput identification procedure validates a new, rapid, unrestricted analytical method for the determination of LCPs.

Synthesis of Hf6Ta2O17 superstructure via spark plasma sintering for improved oxidation resistance of multi-component ultra-high temperature ceramics

Ultra-high temperature ceramics (UHTCs) have shown aspiration to overcome challenges in the thermal protection system (TPS) by designing new materials referred to as multi-component UHTCs (MC-UHTCs) in the compositional space. MC-UHTCs have shown remarkable improvement in oxidation resistance due to the formation of the Hf 6 Ta 2 O 17 superstructure during plasma exposure. Herein, the Hf 6 Ta 2 O 17 superstructure is synthesized via a solid-state reaction between HfO 2 and Ta 2 O 5 powder mixtures during spark plasma sintering (SPS). The compositions chosen are 50 vol% of HfO 2 -50 vol% of Ta 2 O 5 (50HO-50TO) and 70 vol% of HfO 2 -30 vol% of Ta 2 O 5 (70HO-30TO). The phase quantification via Rietveld analysis showed Hf 6 Ta 2 O 17 as a principal phase with some residual Ta 2 O 5 phase in both the samples. The high-temperature thermal stability of the samples was evaluated using high-velocity plasma jet exposure for up to 3 min. 50HO-50TO was able to withstand the intense plasma condition, which is attributed to the higher content of the Hf 6 Ta 2 O 17 phase (∼84%) and lower strain in the Ta 2 O 5 phase. The augmentation in the Hf 6 Ta 2 O 17 phase to 94.7% (in 50HO-50TO) post plasma exposure has been attributed to the invariant transformation from a liquid state to Hf 6 Ta 2 O 17 at temperatures >2500 °C during testing. The mechanical integrity is elucidated from the insignificant change in the hardness ∼13.3 GPa before and 11.2 GPa after plasma exposure of the 50HO-50TO sample. As a result, the Hf 6 Ta 2 O 17 superstructure's thermo-mechanical stability suggests developing novel oxidation-resistant MC-UHTCs in compositional space for reusable space vehicle applications.

Machine learning the vibrational free energy of perovskites

The Helmholtz (vibrational) free energy of ABX3 perovskites is modelled as a 3rd-order polynomial of temperature. Then, highly accurate machine-learning models are trained to predict the stability and phase transition in competing phases of the perovskites.

Alloy corrosion and passivation spanning composition space.

In this short review we highlight the importance and the capabilities of composition spread alloy films (CSAFs) for the high-throughput study and comprehensive understanding of corrosion passivation in multicomponent alloys, AxByC1-x-y, spanning composition space, x ∈ [0, 1] and y ∈ [0, 1 - x]. After first establishing the mechanistic issues associated with corrosion, and the problems arising from the corrosion of metals, we establish the need for further studying and understanding the mechanisms of alloy corrosion and corrosion passivation. In particular, we highlight the development of new combinatorial methods that circumvent the experimental bottleneck associated with preparing, characterizing, and testing many alloy samples having common components at different compositions. We will illustrate the use of CSAFs in studying corrosion across alloy composition space. Because of their structure and inherent composition range, CSAFs enable many novel studies that are otherwise intractable using the traditional methods of preparing and testing one alloy composition at a time.

NanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix.

Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relationship in PNCs due to their wide-ranging composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure relation of a PNC through an intelligent machine-learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the distribution of NPs in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-microstructure relationships in PNCs and other molecular systems.

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

A Sequential Decision Algorithm of Reinforcement Learning for Composite Action Space

The effects of animal transfers on the reproductive success of female white rhinoceroses (Ceratotherium simum simum) kept in European zoos

The Southern white rhinoceros (Ceratotherium simum simum) has been kept in European zoos since the 1960s. However, captive breeding success has been low, with social group composition, group size, and available space all playing a role. Female rhinoceroses that have never bred or not bred for a long time have a particularly increased risk of developing reproductive tract pathologies, often resulting in infertility at a young age. One management measure to stimulate breeding is to transfer non-reproducing animals to other zoos. This study evaluated the success of transfers of 4 – 28 years old white rhinoceroses between European zoos. We analyzed n = 90 (45 males and 45 females) transfers of white rhinoceroses between 1990 and 2018. Fecal progesterone metabolite levels were analyzed for a subset of female rhinoceroses. The success of a transfer was defined as a calf born within five years. The success rate after female transfers was 26.7%; however, when the age limit of transferred females is set at 18 years, the success rate was 44.4%. The success rate after a male transfer was 23.2%. In transferred females, 83% of births occurred within three years after a transfer. Births following the arrival of a new male were distributed over five years. After a male transfer, endocrine data were determined in 26 of 82 females affected by the transfer. Positive development of estrous cycle activity after the arrival of the new bull occurred in 13 females. In summary, the success of the transfers in terms of offspring birth and endocrine stimulation of cycle activity was lower than anticipated, and sometimes a considerable amount of time elapsed before a calf was born. Nonetheless, transfers are essential to promote breeding. The relatively low success of the transfers analyzed in this study relates to the partially advanced age of the white rhinoceroses studied. Transfers of juvenile or adolescent females currently conducted between European zoos reveal a better birth rate than the present study.

Development of an accurate “composition-process-properties” dataset for SLMed Al-Si-(Mg) alloys and its application in alloy design

Al-Si-Mg series alloys are the most common alloys available for additive manufacturing forming with low cracking tendency. However, there is no systematic study on the computational design of SLMed Al-Si-(Mg) alloys due to the huge parameter space of composition and processes. In this paper, a high-quality dataset of SLMed Al-Si-(Mg) alloys containing 176 pieces of data from 50 publications was first established, which recorded the information, including alloy compositions, process parameters, test conditions, and mechanical properties. A threshold value of 35 J/mm3 for energy density (Ed) was then proposed as a criterion to clean the data points with lower ultimate tensile strength (UTS) and elongation (EL). The cleaned dataset consists of a first training/testing dataset with 142 data for model construction and a second testing dataset with 9 data for model verification. After that, four machine learning models were applied to establish the quantitative relation of “composition-processes-properties” in SLMed Al-Si-(Mg) alloys. The MLPReg model was chosen as the optimal one considering its best performance and subsequently utilized to design novel compositions and process parameters for SLMed Al-Si-(Mg) alloys. The UTS and EL of the designed alloy with a maximum comprehensive mechanical property are 549 MPa and 16%, both of which are higher than all the available experimental data. It is anticipated that the present design strategy based on the machine learning method should generally be applicable to other SLMed alloy systems.

Evolutionary design of machine-learning-predicted bulk metallic glasses.

The size of composition space means even coarse grid-based searches for interesting alloys are infeasible unless heavily constrained, which requires prior knowledge and reduces the possibility of making novel discoveries. Genetic algorithms provide a practical alternative to brute-force searching, by rapidly homing in on fruitful regions and discarding others. Here, we apply the genetic operators of competition, recombination, and mutation to a population of trial alloy compositions, with the goal of evolving towards candidates with excellent glass-forming ability, as predicted by an ensemble neural-network model. Optimization focuses on the maximum casting diameter of a fully glassy rod, D max, the width of the supercooled region, ΔT x, and the price-per-kilogramme, to identify commercially viable novel glass-formers. The genetic algorithm is also applied with specific constraints, to identify novel aluminium-based and copper-zirconium-based glass-forming alloys, and to optimize existing zirconium-based alloys.

Efficient exploration of compositional space for high-performance copolymers via Bayesian optimization.

The traditional approach employed in copolymer compositional design, which relies on trial-and-error, faces low-efficiency and high-cost obstacles when attempting to simultaneously improve multiple conflicting properties. For example, designing co-cured polycyanurates that exhibit both moisture and thermal resistance, along with high modulus, is a long-term challenge because of the intrinsic trade-offs between these properties. In this work, to surmount these barriers, we developed a Bayesian optimization (BO)-guided method to expedite the discovery of co-cured polycyanurates exhibiting low water uptake, coupled with higher glass transition temperature and Young's modulus. By virtue of the knowledge of molecular simulations, benchmarking studies were carried out to develop an effective BO-guided method. Propelled by the developed method, several copolymers with improved comprehensive properties were obtained experimentally in a few iterations. This work provides guidance for efficiently designing other high-performance copolymers.

АСПЕКТИ ВПЛИВУ КОЛІРНОГО РІШЕННЯ НА СПРИЙНЯТТЯ ЖИВОПИСНОГО ТВОРУ

The article examines the properties of color on which the essence of perception of the color component of a painting is based. It is clarified how with the help of color and color combinations the necessary atmosphere is created in the picture, the perception of the plot of this or that work is formed, how the viewer’s attention is focused on the main thing, the image of the general color composition is created, the techniques of color using are analyzed and their influence on the perception of the semantic component of the plot. Based on the theoretical analysis of literary sources, the physical, physiological, psychological, and emotional aspects of the influence of color on a person are considered, based on the conclusions of several sciences and scientific disciplines. The article highlights the most essential constituents of the phenomenon of color as the primary visual medium, and draws attention to the role of color science, as a science that systematizes knowledge about the nature of color and light, and types of color harmonies. The question of how local colors, tonal saturation, and intensity of color affect the perception of the composition of the work is clarified. The main characteristics of color as a tool capable of determining planning in a painting and providing subconscious information about phenomena or objects are considered, how the brightness of color shades affects the perception of the world in the picture, and why warm and cold colors of the color wheel are perceived differently in the compositional space of the work. The article raises the question of the color decision influence on the perception of the emotional coloring of a painting, why shades of one color sound different when combined with another, and how color combinations affect a person physically and psychologically on an associative level. The main aim of the article is to get acquainted with aspects of the color component understanding and types of color solutions, where color is considered the most effective means of creating a coherent artistic form and harmonizing space in visual art.

A Data-Driven Framework for Computationally Efficient Integration of Chemical Kinetics Using Neural Ordinary Differential Equations

AbstractA data-driven methodology is introduced for computationally efficient integration of systems of stiff rate equations in chemical kinetics using neural ordinary differential equations (NODE). A systematic algorithm is developed for training data generation and sampling. Subsequently, a novel transformation technique for sampled training data is designed to regularize the neural network parameters, leading to a stable training process. Finally, the NODE network is iteratively trained to learn the accurate neural network representation of chemical kinetics source terms by minimizing the mean absolute error between the true and predicted solutions. The computational efficiency and accuracy of the NODE network are evaluated by simulating the evolution of the thermochemical state of a constant pressure homogeneous hydrogen-air reactor. The combustion of hydrogen in air is described by a finite-rate mechanism including 9 chemical species and 21 reaction steps. The NODE network shows excellent multi-step prediction accuracy for a wide range of initial temperatures and equivalence ratios, spanning the composition space of real flames. The NODE also exhibit a significant reduction in numerical stiffness of the system, enabling the utilization of explicit solvers for integration. The present simulation results using NODE demonstrate up to 70% speed up in computation time compared to direct integration of the chemical mechanism with at most 3.16% relative error in ignition delay time.

A review on high-throughput development of high-entropy alloys by combinatorial methods

High-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches. This review highlights the progress in combinatorial high-throughput studies towards rapid prediction, manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to HEAs and their unique properties. Then, this review describes high-throughput computational methods such as machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The next section of this review presents advances in combinatorial synthesis of material libraries by additive manufacturing for efficient development of high-performance HEAs at bulk scale. The final section of this review discusses the high-throughput characterization techniques that are used to accelerate the material property measurements for systematic understanding of the composition-processing-structure-property relationships in combinatorial HEA libraries.

Linking processing parameters with melt pool properties of multiple nickel-based superalloys via high-dimensional Gaussian process regression

A physics-based model is used to predict the melt pool properties in the laser-directed energy deposition of several nickel-based superalloys for different process parameters. The input space is high-dimensional, consisting of a common 19-dimensional composition space for each alloy and the process parameters (laser power and scan velocity). Gaussian Process-based regression frameworks are developed by training surrogates on data generated by a validated analytical model. These surrogates are thereafter used to predict and define relationships between the composition, resultant thermophysical properties, process parameters, and the subsequent melt pool property. The probabilistic predictions are augmented by uncertainty quantification and sensitivity analysis to substantiate the findings further.

Exploration of a NiFeV multi-metal compositional space for the oxygen evolution reaction

We performed a high throughput investigation of a Ni, Fe and V library for the oxygen evolution reaction. The study provides insights into the composition-activity relationship and guides rapid design and development of high performing catalysts.