Large Compositional Space Research Articles

High Entropy Oxide (CrMnFeNiMg)3 O4 with Large Compositional Space Shows Long-Term Stability as Cathode in Lithium-Sulfur Batteries.

The repeated formation and irreversible diffusion of liquid-state lithium polysulfides (LiPSs) are the primary challenges in the development of high-energy-density lithium-sulfur battery (LSB). An effective strategy to alleviate the resulting polysulfide loss is critical for the stability of LSBs. In this regard, high entropy oxides (HEOs) appear as a promising additive for the adsorption and conversion of LiPSs owing to the diverse active sites, offering unparalleled synergistic effects. Herein, we have developed a (CrMnFeNiMg)3 O4 HEO as a functional polysulfide trapper in LSB cathode. The adsorption of LiPSs by the metal species (i. e., Cr, Mn, Fe, Ni, and Mg) in the HEO takes place through two different paths and leads to enhanced electrochemical stability. We demonstrate that the optimal sulfur cathode with the (CrMnFeNiMg)3 O4 HEO attains a high peak and reversible discharge capacities of 857 mAh g-1 and 552 mAh g-1 , respectively, at a cycling rate of C/10, a long cycle life of 300 cycles, and a high rate performance at the cycling rates from C/10 to C/2.

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

Transfer learning aided high-throughput computational design of oxygen evolution reaction catalysts in acid conditions

Sluggish oxygen evolution reaction (OER) in acid conditions is one of the bottlenecks that prevent the wide adoption of proton exchange membrane water electrolyzer for green hydrogen production. Despite recent advancements in developing high-performance catalysts for acid OER, the current electrocatalysts still rely on iridium- and ruthenium-based materials, urging continuous efforts to discover better performance catalysts as well as reduce the usage of noble metals. Pyrochlore structured oxide is a family of potential high-performance acid OER catalysts with a flexible compositional space to tune the electrochemical capabilities. However, exploring the large composition space of pyrochlore compounds demands an imperative approach to enable efficient screening. Here we present a high-throughput screening pipeline that integrates density functional theory calculations and a transfer learning approach to predict the critical properties of pyrochlore compounds. The high-throughput screening recommends three sets of candidates for potential acid OER applications, totaling 61 candidates from 6912 pyrochlore compounds. In addition to 3d-transition metals, p-block metals are identified as promising dopants to improve the catalytic activity of pyrochlore oxides. This work demonstrates not only an efficient approach for finding suitable pyrochlores towards acid OER but also suggests the great compositional flexibility of pyrochlore compounds to be considered as a new materials platform for a variety of applications.

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.

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni–Ti Alloys in Large Compositional and Processing Space

Data-Driven Study of Shape Memory Behavior of Multi-Component Ni–Ti Alloys in Large Compositional and Processing Space

An experimentally driven high-throughput approach to design refractory high-entropy alloys

High-entropy alloy (HEA) design strategies have been limited to theoretical/computational approaches due to their compositional complexity and extremely large compositional parameter space. In this work, we developed an experimentally driven, high-throughput, HEA design approach using a physical vapor deposition (PVD) technique and coupled it with nanomechanical testing to accelerate material design for structural applications. The PVD technique enabled the formation of a compositional gradient across a thin-film sample. Specifically, a 10 cm wafer was used to manufacture a continuous set of 80 HEA compositions within the Nb-Ti-V-Zr family using a single deposition cycle. By using the solid-solution strengthening theory and estimated parameter properties, the strength and ductility of these HEA compositions were quantitatively determined/predicted and then experimentally verified by nano-indentation hardness test. Consequently, 7 refractory HEA compositions were successfully down-selected, which has a high propensity to have a balanced mechanical property.

Enhanced Passivity and Resistance to Pitting of New Cr-Fe-Co-Ni-Mo Multi-Principal Element Single-Phase Alloys

Extensive thermodynamic calculations exploring the large composition space of the Cr-Fe-Co-Ni-Mo system were performed in order to design new multi-principal element single-phase fcc alloys with enhanced passivity and resistance to localised corrosion.This presentation will cover several aspects from design to production, passivation, surface analysis and pitting resistance of alloys with up to 15 at.% Mo . The newly designed and produced quinary alloys, Cr25Fe25Co5Ni40Mo5 and Cr15Fe10Co5Ni60Mo10 (at.%) show superior resistance to localised corrosion in chloride solution. The chemical states, composition and stratification of passive films were investigated by X-ray Photoelectron Spectroscopy (XPS) and Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS). The resistance to pitting was measured after pre-passivation of the alloy surfaces in Cl--free acidic solution.Stratification of the passive films was observed, with Cr, Fe, Ni,Mo oxides and hydroxides in the outer layer and Cr oxide in the inner layer.The amounts and distribution of chloride ions that penetrated into the passive films were also investigated. In-depth profiles show that, on the Mo-bearing alloys, chloride ions do not penetrate the Mo-enriched outer layer of the oxide film.A direct link between the structural and chemical nature of passive films and the resistance to pitting in chloride solution was observed. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (advanced grant agreement # 741123)

Designing multinary noble metal‐free catalyst for hydrogen evolution reaction

AbstractThe hydrogen evolution reaction (HER), the key reaction for electrocatalytic production of hydrogen, is of fundamental importance due to its simplicity yet is very important for renewable energy. Notwithstanding, Pt is still the main catalyst for this reaction, which is not practical for the industrial deployment of this technology owing to the high cost and scarcity of Pt. The successful synthesis of high entropy alloy (HEA) nanoparticles opens a new frontier for the development of new catalysts. Herein we investigate the design of a multinary noble metal‐free HER catalyst based on earth‐abundant elements Co, Mo, Fe, Ni, and Cu. Using a machine learning (ML) approach in conjunction with first‐principles methods, we build a model that can rapidly compute the hydrogen adsorption energy on the alloyed surfaces with high fidelity. Within the large composition space of the CoMoFeNiCu HEA, a large number of alloy combinations are shown to optimally bind hydrogen with a high probability. Further, most of these alloy compositions are found stable against dissociation into intermetallics, and hence synthesizable as a solid solution, by virtue of a large mixing entropy compared to mixing enthalpy and a small lattice mismatch between the elements. This finding is partly consistent with recent experimental results that synthesized five different CoMoFeNiCu HEA compositions. Our study underscores the significant impact that computational modeling and ML can have on developing new cost‐effective electrocatalysts in the nearly‐infinite materials design space of HEAs, and calls for experimental validation.

A first-principles-based high fidelity, high throughput approach for the design of high entropy alloys

Here, we present a preselected small set of ordered structures (PSSOS) method, a first principles-based high fidelity (HF), high throughput (HT) approach, for fast screening of the large composition space of high entropy alloys (HEAs) to select the most energetically stable, single-phase HEAs. Taking quinary AlCoCrFeNi HEA as an example system, we performed PSSOS calculations on the formation energies and mass densities of 8801 compositions in both FCC and BCC lattices and selected five most stable FCC and BCC HEAs for detailed analysis. The calculation results from the PSSOS approach were compared with existing experimental and first-principles data, and the good agreement was achieved. We also compared the PSSOS with the special quasi-random structures (SQS) method, and found that with a comparable accuracy, the PSSOS significantly outperforms the SQS in efficiency, making it ideal for HF, HT calculations of HEAs.

Discovery of ferromagnetism in new multicomponent alloy Ti–Nb–Cr–Ru

We report the discovery of ferromagnetism in the cubic CsCl-type Ti21∼25Nb20∼24Cr5∼10Ru∼49 multicomponent alloy. In metals, the appearance of ferromagnetism due to the Cr magnetic moment is a rare phenomenon. The purest sample shows ferromagnetism with the Curie temperature of 38 K. The effective magnetic moment and the Weiss temperature are 3.67 μB/Cr and 58 K, respectively, derived from the temperature dependence of dc magnetization. These values mean the ferromagnetic exchange interaction between the localized Cr magnetic moments. The ferromagnetic nature is also confirmed by the isothermal magnetization curve with the highest magnetization of 1.1 μB/Cr at 2 K. The electronic structure calculation also supports a ferromagnetic ground state in the CsCl-type structure. We further investigated the effect of elemental substitution on the ferromagnetic behavior. The partial substitution of Pd for Ru heavily suppresses the Curie temperature, indicating that the Ru atom may play an essential role in sustaining ferromagnetism. Ti21∼25Nb20∼24Cr5∼10Ru∼49 would be the first example of the ferromagnetic Cr-containing multicomponent alloy, and this study shows the usefulness of the large compositional space in exploring novel phenomena.

Optimizing Electrocaloric Effect in Barium Titanate-based Room Temperature Ferroelectrics: Combining Landau Theory, Machine Learning and Synthesis

The search for ferroelectrics with optimized electrocaloric effects is often hindered due to the large compositional space and lack of reliable property data. Phenomenological theory, such as the Landau approach, then becomes useful, provided such models can be reliably parametrized with experimental data. We propose an approach in which we combine a surrogate model formed from widely available polarization data to predict entropy changes using polarization from the machine learned model as input to the Landau model. Our objective is to search for room temperature BaTiO3 based calorics, and we validate our approach by synthesizing compounds guided by optimal experimental design using the surrogate model for polarization. We iterate our design loop four times, predicting and synthesizing four compounds, the electrocaloric effects of which are estimated using both indirect and direct characterization. The compositions show competitive electrocaloric performance at room temperature with a large adiabatic temperature change ΔT∼0.6 K at 20 kV/cm in a wide temperature window (∼30 K with 90%ΔT). Moreover, we find that the homogeneous Landau models are largely predictive in the linear regime where the change in entropy varies quadratically with polarization. We discuss the role of nonlinear and diffusive effects for larger entropy behavior. The proposed approach shows promise in the use of theory or physics based models to bridge with output from machine learning methods when limited data is available for a property of interest.

Composition design of high-entropy alloys with deep sets learning

High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.

Theoretical Optimization of Compositions of High‐Entropy Oxides for the Oxygen Evolution Reaction**

AbstractHigh‐entropy oxides are oxides consisting of five or more metals incorporated in a single lattice, and the large composition space suggests that properties of interest can be readily optimised. For applications within catalysis, the different local atomic environments result in a distribution of binding energies for the catalytic intermediates. Using the oxygen evolution reaction on the rutile (110) surface as example, here we outline a strategy for the theoretical optimization of the composition. Density functional theory calculations performed for a limited number of sites are used to fit a model that predicts the reaction energies for all possible local atomic environments. Two reaction pathways are considered; the conventional pathway on the coordinatively unsaturated sites and an alternative pathway involving transfer of protons to a bridging oxygen. An explicit model of the surface is constructed to describe the interdependency of the two pathways and identify the composition that maximizes catalytic activity.

Theoretical Optimization of Compositions of High-Entropy Oxides for the Oxygen Evolution Reaction.

High‐entropy oxides are oxides consisting of five or more metals incorporated in a single lattice, and the large composition space suggests that properties of interest can be readily optimised. For applications within catalysis, the different local atomic environments result in a distribution of binding energies for the catalytic intermediates. Using the oxygen evolution reaction on the rutile (110) surface as example, here we outline a strategy for the theoretical optimization of the composition. Density functional theory calculations performed for a limited number of sites are used to fit a model that predicts the reaction energies for all possible local atomic environments. Two reaction pathways are considered; the conventional pathway on the coordinatively unsaturated sites and an alternative pathway involving transfer of protons to a bridging oxygen. An explicit model of the surface is constructed to describe the interdependency of the two pathways and identify the composition that maximizes catalytic activity.

High-Entropy Laminate Metal Carbide (MAX Phase) and Its Two-Dimensional Derivative MXene

High-entropy (HE) ceramics, by analogy with HE metallic alloys, are an emerging family of multielemental solid solutions. These materials offer a large compositional space, with a corresponding large range of properties. Here, we report the experimental realization of a 3D HE MAX phase, Ti1.0V0.7Cr0.05Nb1.0Ta1.0AlC3, and a corresponding 2D HE MXene in the form of freestanding flakes of average composition Ti1.1V0.7CrxNb1.0Ta0.6C3Tz (Tz = −F, −O, −OH), as produced by selective removal of Al from the HE MAX phase in aqueous hydrofluoric acid (HF). Initial tests on HE MXene “paper” electrodes show their high potential as electrode materials in supercapacitors through volumetric and gravimetric capacitances of 1688 F/cm3 and 490 F/g, respectively, originating from a combination of diffusion- and surface-controlled charge storage processes. The introduction of the HE concept into the field of 2D materials suggests a wealth of future 2D materials and applications.

Mapping the creep life of nickel-based SX superalloys in a large compositional space by a two-model linkage machine learning method

Accurate prediction of the creep life is important during the alloy design and optimization of nickel-based single crystal superalloys, especially for those with expensive alloying elements such as Re and Ru. In this study, a two-model linkage method was developed to predict the creep life of nickel-based single crystal superalloys through a data-driven machine learning approach, based on a small dataset collected from literatures. Owing to the small data amount, the precision of the model which was constructed with the alloy composition, creep temperature, and creep stress as inputs and used to predict the creep life is low, but the precision of the model which was constructed with the alloy composition, creep temperature, and creep life as inputs and used to predict the creep stress is relatively high. The high-precision model was applied to judge and adjust the prediction results of the low-precision model, thus effectively improving the accuracy of the overall creep life prediction. Moreover, the well-trained model-linkage was used to analyze, as a showcase, the interactive effects of alloying elements on the creep life of nickel-based single crystal superalloys. The prediction results provided a clear mapping of synergistic effects in the investigated compositional space, which was very consistent with previous, experimental results. This consistency thus confirms the effectiveness of the two-model linkage method in studying synergistic effects of multiple alloying elements. This study will benefit the compositional design and optimization of next-generation nickel-based single crystal superalloys.

Gelation phase diagrams of colloidal rod systems measured over a large composition space

Rheological modifiers tune product rheology with a small amount of material. To effectively use rheological modifiers, characterizing the rheology of the system at different compositions is crucial. Two colloidal rod system, hydrogenated castor oil and polyamide, are characterized in a formulation that includes a surfactant (linear alkylbenzene sulfonate) and a depletant (polyethylene oxide). We characterize both rod systems using multiple particle tracking microrheology (MPT) and bulk rheology and build phase diagrams over a large component composition space. In MPT, fluorescent particles are embedded in the sample and their Brownian motion is measured and related to rheological properties. From MPT, we determine that in both systems: (1) microstructure is not changed with increasing colloid concentration, (2) materials undergo a sol–gel transition as depletant concentration increases and (3) the microstructure changes but does not undergo a phase transition as surfactant concentration increases in the absence of depletant. When comparing MPT and bulk rheology results different trends are measured. Using bulk rheology we observe: (1) elasticity of both systems increase as colloid concentration increases and (2) the storage modulus does not change when PEO or LAS concentration is increased. The differences measured with MPT and bulk rheology are likely due to differences in sensitivity and measurement method. This work shows the utility of using both techniques together to fully characterize rheological properties over a large composition space. These gelation phase diagrams will provide a guide to determine the composition needed for desired rheological properties and eliminate trial-and-error experiments during product formulation.

From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach.

Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature (Tg) are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that high recovery stress usually means high Tg. For a few TSMPs with high recovery stress, their Tg values are close to the decomposition temperature, and thus, the shape memory effect cannot be triggered safely and effectively. While machine learning (ML) has served as a useful tool to discover new materials and drugs, the grand challenge of using ML to discover new TSMPs persists in the very limited data available. Here, we report an enhanced ML approach by combining the transfer learning-variational autoencoder with a weighted-vector combination method. By learning a large data set with drug molecules in a pretraining process, we were able to effectively map the TSMPs to a hidden space that is much closer to a Gaussian distribution. Through this approach, we created a large compositional space and were able to discover five new types of UV-curable TSMPs with desired properties, one of which was validated by the experiments. Our contribution includes (1) representing the features of TSMPs by drug molecules to overcome the barrier of a limited training data set and (2) developing a ML framework that is able to overcome the barrier of mapping the molar ratio information. It is shown that this approach can effectively learn TSMP features by utilizing the relatedness between the data-scarce (and biased) TSMP target and data-abundant drug source, and the result is much more accurate and more robust than the benchmark set by the support vector machine method using direct label encoding and Morgan encoding. Therefore, it is believed that this framework is a state-of-the-art study in the TSMP field. This study opens new opportunities for discovering not only new TSMPs but also other thermoset polymers.

Ordering and phase separation in multi-principal-element metallic alloys: Contribution from mean-field atomic-scale modelling and simulation

Multi-principal-element alloys (MPEAs) are a wide class of materials currently at the center of numerous investigations. Key-issues with MPEAs are related with controlling the onset of (i) long-range order and (ii) phase separation, both processes being in general detrimental to the properties. While trends (i) and (ii) are strongly dependent on the precise proportions of metallic elements around the reference equiatomic MPEA, the tremendously large composition space makes it a tricky task to heuristically identify optimized MPEAs. In practice, this often leads to undesired second-phase ordered particles. This deficiency emphasizes the relevance of modelling and simulation tools designed to facilitate the exploration of MPEA composition spaces. In this atomic-scale work, we propose a “composition-constrained” point mean-field formalism resting on alloy pair energetics, to investigate trends (i) and (ii). This formalism is currently applicable to a wide panel of such systems built from ~ 30 chemical elements. Its application to several MPEAs along the AlCoCrFeNi → AlCrFeMnNi → AlCrFeMnMo sequence evidences striking differences between these systems, and demonstrates its efficiency to get at-a-glance information about (i) and (ii) features for various MPEAs. The flexibility of the proposed simulation approach makes it easily employable in conjunction with experiments on well-equilibrated MPEA samples, for thermodynamic characterizations of MPEAs.

Composition-dependent structure and properties of 5- and 15-element high-entropy alloy nanoparticles

High-entropy alloy nanoparticles (HEA NPs) are an area of interest due to their interesting structural features, large compositional space, and high potential for catalytic applications. While many HEA NPs have been successfully synthesized and characterized, little is known about the impact of composition on structural features. This work utilizes element-specific X-ray spectroscopy techniques to compare 5- and 15-element HEA NPs with shared elements, aiming to understand the impact that adding additional elements to an HEA has on its structure and properties. Here, we report significant differences between HEA-5 and HEA-15. In particular, the 3d elements show different oxidation behavior associated with different bonding properties. Varied bonding properties are also observed in the 4d and 5d elements. These results provide an understanding of the impact composition plays on the HEA structure, which will serve to aid in the fine-tuning of structure and properties of HEAs through adjusting their composition.