Composition Space Research Articles

Ab initio study of the lattice distortion and the vacancy defects in multi-anion oxycarbonitride ceramics TiCNO and TiZrCNO

Multicomponent ceramics have received much attention due to the tunning physicochemical properties and the huge compositional space, while multicomponent ceramics with the mixing of multi-anion are rarely studied. In this work, the local lattice distortion and vacancy formation energy in TiCNO and TiZrCNO are systematically investigated by density functional theory calculations combining with the special quasi-random structure model. The present results show that multi-anionic mixed oxycarbonitride ceramics exhibit obvious local lattice distortion. The quantified displacive distortions of C, N, and O in TiCNO are relatively large, being in order of C < N < O, although metallic elements Ti has larger distortion than nonmetals. Within incorporative mixing of Zr in cation sublattice, the distortions of Ti, C, N, and O increase evidently, the distortion degree for nonmetal elements is still in order of C < N < O. The distortion of Ti is still the largest, whereas Zr possesses almost the smallest distortion. The electronic structure shows a close association with the degree of distortion, the larger DOS at the Fermi level corresponds to the larger distortion. The vacancy defects are further studied. Our consequences show that the vacancy formation energies of non-metal elements with large local distortions are relatively low, suggesting that the local chemical environment profoundly affects the vacancy formation energy and defect-related properties. The present study reveals the important correlation of lattice distortion with electronic structure and vacancy generation, so it is valuable to deepen the understanding of the excellent properties of multicomponent ceramics with multi-anion mixing, and develop novel multicomponent ceramics with polyanionic structures.

High-entropy alloy nanocrystals with low-angle grain boundary for superb plastic deformability and recoverability

High entropy alloys (HEAs) have received widespread attention as structural and functional materials owing to their large atomic lattice distortion and vast compositional space. Recently, nanoscale HEAs have been proven to exhibit exceptional combinations of mechanical properties, thermal stability, and oxidation resistance. The structural and functional stabilities of nanoscale HEAs under practical loading conditions are vital to their applications. Here, based on a grain boundary (GB) design strategy, we introduced custom-designed low-angle GBs (LAGBs) into quinary face-centered cubic HEA nanocrystals, intending to improve the mechanical stability of nanoscale HEAs under external loading through molecular dynamics simulations. Using CuCoNiPdFe and FeNiCrCoCu HEAs as examples, we reveal two distinct LAGB-mediated deformation behaviors of nanoscale HEAs, which stem from the differences in lattice distortion and shear modulus of the materials. In the case of CuCoNiPdFe, the LAGB is composed of separate Shockley dislocations, endowing the material with extraordinary plastic deformability and structural stability. In the case of FeNiCrCoCu, the LAGB contains a series of parallel stacking faults cross-linked immobile dislocation segments, which severely damages the plastic deformability of the material. Based on the atomistic understanding of LAGB structure and migration behavior, we have screened out a range of material systems with small lattice distortion and shear modulus, realizing superb plastic deformability and structural recoverability in HEAs designed with LAGBs. These findings promote the understanding of GB-mediated deformation mechanisms in HEAs and provide insights into the structural and composition design of HEA nanomaterials.

Study on Behaviour of a Composite Space Truss Analytically

Study on Behaviour of a Composite Space Truss Analytically

A novel high-entropy MXene Ti1.1V1.2Cr0.8Nb1.0Mo0.9C4Tx for high-performance supercapacitor

Both MXenes and high-entropy ceramics are booming material families. The rich surface functional groups and elemental controllability of MXenes, as well as the rich composition space of high-entropy ceramics provide these materials unexpected chemical properties, and thus they have great potential in energy storage. Herein, we successfully synthesized a novel high-entropy MXene (HE-MXene), Ti1.1V1.2Cr0.8Nb1.0Mo0.9C4Tx for the first time. The aberration-corrected scanning transmission electron microscopy, density functional theory calculation, and super energy-dispersive X-ray spectroscopy together demonstrate that the five transition metals are uniformly dispersed in one MX slab in the form of solid solutions. The Ti1.1V1.2Cr0.8Nb1.0Mo0.9C4Tx exhibits high capacity as a supercapacitor electrode with a gravimetric capacitance of 284.6 F g−1 at a current density of 1 A g−1 and also shows outstanding stability. This work proves the existence of M5X4Tx HE-MXene and further adds a new member to the MXene family, providing support for its application in other fields.

Aluminum concentration effect on the oxidation behavior of CrAl coatings investigated by high-throughput method

The compositional gradient CrAl coating on Zry-4 alloys substrate was fabricated by the high-throughput method and its oxidation behavior in 1200 °C steam environment has been studied. The results divide the CrAl composition space into four regions according to the different oxidation behavior. The stable and continuous Cr2O3/Al2O3 layer can form in the composition of 21.9 Al at%, which shows excellent oxidation resistance. The oxidation mechanism of the compositional gradient CrAl coating is systematically discussed.

Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components

The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope method (CEM), which constructs all liquid phase equilibria over the whole composition space for a given system with an arbitrary number of components. For this matter, the composition space is discretized and the convex envelope of the Gibbs energy graph is computed. Employing the tangent plane criterion, all liquid phase equilibria can be determined in a robust way. The generalized CEM is described within a mathematical framework and it is shown to work numerically with various examples of up to six components from the literature.

Coupling mixture designs, high-throughput experiments and machine learning for accelerated exploration of multinary systems

Current societal challenges, such as climate change and resource depletion, highlight an unprecedented need for disruptive innovation in materials science. Significant breakthroughs are expected in multinary materials whose efficient exploration necessitates dedicated strategies. The exploration of a refractory high entropy alloy Nb-Ti-Zr-Cr-Mo is proposed here as test case for a new strategy. Based on the proven methodology of mixture design and on combinatorial thin film metallurgy, the composition space is explored by a limited number of chosen gradients to build an alloy library comprising hardness and ductility, two antagonistic properties. The workflow is showcased here by studying the properties of the as-grown graded film, which presents wide amorphous domains and contrasted mechanical properties. This experimental dataset then trains machine learning models to provide continuous predictions of the alloy properties over the entire composition space. We show that optimal alloy properties are expected close to the binary edges of the quinary.

Iterative cluster expansion approach for predicting the structure evolution of mixed Ruddelsden-Popper oxides La2−xSrxNi1−yFeyO4±δ

Layered perovskites viz. Ruddlesden-Popper (RP) compounds have attracted a significant research interest lately due to their superior electrocatalytic activities, on top of their rich physics. Although the versatility of their compositional space is the key to tune their stability and applicability, its complexity also makes the understanding and design of new functional RP-based materials difficult. Herein by parameterizing a cluster expansion model Hamiltonian with density-functional inputs, we have successfully scanned over the complex configurational space of an RP phase, taking ${\mathrm{La}}_{2\ensuremath{-}x}{\mathrm{Sr}}_{x}{\mathrm{Ni}}_{1\ensuremath{-}y}{\mathrm{Fe}}_{y}{\mathrm{O}}_{4\ifmmode\pm\else\textpm\fi{}\ensuremath{\delta}}$ as an example. This surrogate model is then used to predict the energies of unknown compositions and performing Metropolis Monte Carlo sampling for finding the ground-state structures. By analyzing these stable configurations at different $x, y$'s, and $\ensuremath{\delta}$=0, 0.125, we found that apical vacancies in the rock-salt layer are more favorable due to lesser steric forces but changes in $A$-site oxidation state due to Sr substitution can facilitate equatorial O-vacancy formation even in the perovskite layers. Our results uncover the structural evolution of RP oxides with increasing $A$-site (Sr) and $B$-site (Fe) substitutions. We find that a critical Sr concentration is required to stabilize Fe in these systems. With increasing Sr concentration, the $A$-site charge state changes gradually from ${\mathrm{La}}^{+3}$ to ${\mathrm{Sr}}^{+2}$, which facilitates both O-vacancy formation and stabilization of the $B$-site dopants. The findings further highlight the effects of configurational changes for the same composition on electronic structure. Thus, our work clearly demonstrates the importance of exploring configurational space of these complex oxides when using electronic-structure features as descriptors of their catalytic and other functional properties.

Ferrite precipitation in quaternary Fe–C–X1–X2 systems using high-throughput approaches

This study investigates the effect of composition on ferrite growth kinetics in quaternary Fe–C–X1–X2 systems (X: Ni, Cr, Mo) using a high-throughput methodology. This study provides the largest dataset to date on ferrite growth kinetics in multi-component steels, offering novel insight into the behavior of these complex systems. To this end, high-energy X-ray diffraction is utilized to gather kinetic data in situ along composition gradients, leading to the measurement of phase transformation kinetics maps in compositional space. The obtained data is compared to predictions from various models describing ferrite growth kinetics in low-alloy steels. The modified "three-jump" solute drag model is shown to describe best the ferrite growth kinetics in these quaternary systems, without the need for additional calibration or fitting parameters. The success of this model is attributed to its consideration of individual solute interactions with the interface and inter-elemental interactions. The findings of this study provide valuable insight for robust modeling of phase transformations and microstructural evolution in multi-component steels, a critical tool in accelerating alloy optimization and in enhancing process control.

Alloying Effect on Elastic and Mechanical Properties of Refractory Medium-Entropy Alloys from First-Principles Calculations

There is still a challenge to rapidly and accurately acquire the composition dependence of mechanical properties of multi-principal element alloys due to their huge composition space and complex electronic structures. Aiming to straightforwardly tune the mechanical properties through alloying, we systematically calculate the elastic and mechanical properties for four selected body-centered-cubic (bcc) Ti–Nb–M (M = Zr, Mo, Sn, Ta) refractory medium-entropy alloys (RMEAs) using a first-principles method. It is shown that the elastic stability of the bcc RMEAs can be significantly (weakly) improved with the increase in Nb, Mo, and Ta (Sn) content, but it is insensitive to Zr. The elastic stability, elastic modulus, hardness, and strength generally enhance, whereas Pugh’s ratio and Zener anisotropy (AZ) decrease with the increase in valence electron concentration. Additionally, there is a correlation between the magnitude of the AZ and the shape of the single-crystal Young’s modulus (E[hkl]). The largest (smallest) E[hkl] appears in the <111> (<100>) direction. Particularly, the most isotropic Ti60Nb20Mo20 has the highest hardness and strength among all the Ti–Nb–M alloys. Moreover, the change of elastic properties induced by alloying is related to the change of density of states. This work sheds deep light on the design of high-performance Ti-based multi-principal element alloys.

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part I: design principles and rapid down-selection

Refractory metal-based multi-principal element alloys (MPEAs) are compelling materials for high-temperature (1000–2000 K) structural applications. However, only a minuscule fraction of their vast and heterogeneous compositional design space has been explored, leaving many potentially interesting alloys undiscovered. In this two-part work, a large region of the 11-element Al-Cr-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr design space is computationally explored to identify refractory MPEAs with simultaneously high yield strength or specific yield strength and body-centered cubic (BCC) solid solution stability. In Part I, two case studies illuminate key factors and considerations in the yield strength versus phase stability tradeoff, provide guidelines for narrowing the expansive design space, and identify many candidates predicted to be stronger than refractory MPEAs reported to date, with BCC phase stability. Our findings indicate that medium entropy ternary alloys can outperform alloys with more elements and highlight the importance of exploring regions away from the equiatomic center of composition space.

Computational discovery of ultra-strong, stable, and lightweight refractory multi-principal element alloys. Part II: comprehensive ternary design and validation

Here the discovery of refractory multi-principal element alloys (MPEAs) with high-temperature strength and stability is pursued within a constrained and application-relevant design space. A comprehensive approach is developed and applied to explore all 165 ternary systems in the Al-Ce-Fe-Hf-Mo-Nb-Ta-Ti-V-W-Zr family. A subset of ternary systems that contain large areas in composition–temperature space with high strength and robust BCC phase stability is found. Twelve sets of high-performing alloys are identified, each set optimized for one combination of phase constraint, optimization target, and temperature range. Preliminary mechanical tests support the viability of the method. This work highlights the importance of considering phase stability, exploring non-equiatomic regions of composition space, and applying application-relevant constraints. Parts I and II provide three down-selection techniques for identifying high-performing BCC refractory MPEAs, design guidelines, and many candidates predicted to have BCC phase stability and strengths 2–3 times higher than any reported to date.

Overpotential from Cosolvent Imbalance in Battery Electrolytes: LiPF6 in EMC:EC.

Most liquid lithium-ion-battery electrolytes incorporate cosolvent blends, but the dominant electrochemical transport models adopt a single-solvent approximation, which assumes in part that nonuniform cosolvent ratios do not affect cell voltage. For the popular electrolyte formulation based on ethyl-methyl carbonate (EMC), ethylene carbonate (EC), and LiPF6, we perform measurements with fixed-reference concentration cells, finding appreciable liquid-junction potentials when only the cosolvent ratio is polarized. A previously reported junction-potential correlation for EMC:LiPF6 is extended to cover much of the ternary composition space. We propose a transport model for EMC:EC:LiPF6 solutions grounded in irreversible thermodynamics. Thermodynamic factors and transference numbers are entwined in liquid-junction potentials, but concentration-cell measurements determine observable material properties we call junction coefficients, which appear in the extended form of Ohm's law that accounts for how composition changes induce voltage drops. Junction coefficients of EC and LiPF6 are reported and illustrate the extent to which ionic current induces solvent migration.

Accelerated search for new ferroelectric materials

We report the development of a combined machine learning and high-throughput density functional theory (DFT) framework to accelerate the search for new ferroelectric materials. The framework can predict potential ferroelectric compounds using only elemental composition as input. A series of machine-learning algorithms initially predict the possible stable and insulating stoichiometries with polar crystal structures, necessary for ferroelectricity, within a given chemical composition space. A classification model then predicts the point groups of these stoichiometries. A subsequent series of high-throughput DFT calculations finds the lowest-energy crystal structure within the point group. As a final step, nonpolar parent structures are identified using group theory considerations, and the values of the spontaneous polarization are calculated using DFT. By predicting the crystal structures and the polarization values, this method provides a powerful tool to explore new ferroelectric materials beyond those in existing databases.

Predicting the non-linear response of composite materials using deep recurrent convolutional neural networks

This paper presents a novel framework to predict the full non-linear response of composite materials using Deep Recurrent Convolutional Neural (DCRN) Networks. The framework is based on a Representative Volume Element (RVE) database populated by sampling the composite design space in terms of layups, defects/variabilities and loading conditions. Several sources of material non-linearity are included in these models such as matrix damage, delamination, fibre failure and shear non-linearity. A DCRN Network architecture is proposed which combines convolutional layers, for spatial features detection, with Long/Short Term Memory layers, for material loading history dependencies. The models’ database consists of images of each RVE model, representing the layup and variables such as the presence of wrinkles, gaps or voids, and the homogenised 3D stress/strain curves. DCRN Networks are trained to predict the full 3D stress response of a laminated composite using the information from the RVE models database. Two formulations for the DCRN Network are studied, a pointwise prediction formulation and a time-marching prediction formulation. The two formulations are compared based on accuracy and robustness. The results show that both approaches can accurately predict the non-linear response of laminated composites.

Compositive role of refractory element Mo in improving strength and ductility of face-centered-cubic complex concentrated alloys

Complex concentrated alloys (CCAs) with a face-centered-cubic (FCC) structure exhibit remarkable mechanical properties, introducing the expansion of compositional space in alloy design for structural materials. The formation of a single solid-solution phase is enabled by configuring various 3d-transition elements, while doping other elements even of a small portion generally leads to the formation of brittle intermetallic compounds. Herein, we demonstrate through a systematic investigation of single FCC (CoNi)100-xMox alloys that a wide range of refractory element Mo can simultaneously improve the strength and ductility while sustaining the solid-solution structure. The addition of Mo with a larger atomic size than those of 3d-transition elements introduces severe lattice distortion in the FCC lattice and causes grain-boundary segregation enriched by Mo atoms. In addition, increasing Mo content effectively reduces the stacking fault energy (SFE). The increased lattice distortion with Mo content enhances the solid-solution strengthening of the alloys. Besides, along with reduced SFE and stabilization of the dislocation emission site by grain-boundary segregation, this elevated solid-solution strengthening increases grain-boundary strengthening, reaching a yield strength of ∼1 GPa. Moreover, the reduction of SFE with increasing Mo results in the transition of dislocation substructures and the refinement of deformation twins, allowing for enhanced strain-hardening capability and thus ∼1.3 GPa tensile strength and ∼50% ductility. Such compositive and synergetic effects of refractory element Mo enable the CCAs with a single FCC solid solution to overcome the strength and ductility trade-off.

Spatial-temporal evolution and driving mechanism of rural production-living-ecological space in Pingtan islands, China

With the continuous penetration of global networking and post-industrialism, the island rural spatial evolution becomes an important issue in comprehensive geography. Studying the microscale evolution and driving mechanism of island rural space is of great significance for promoting its reconstruction and revitalizing island rural communities. This paper constructed a classification system of island rural space from the production-living-ecological perspective. Taking Beigang Village located in China's southeast coast as an example, we analyzed the dynamic mechanism of island rural spatial evolution from traditional agricultural space to multi-functional composite space. The findings indicated that the drastic evolution and reconstruction occurred in the production-living-ecological space of Beigang from 2006 to 2021, manifested as the following aspects: production space was expanded to the outward tourist service space from inward agricultural production space, living space was evolved into commercial space from daily space to provide tourism services, and ecological space was constantly compacted and expanded in tourist development construction and ecological redline regulation. The island rural spatial pattern evolution was the result of the game among policy, capital, society, and organization, manifested as internal roles and external feedback of governmental policy guidance, tourism capital incentive, innovation of business philosophy, and grassroots organizational initiative. During the process, evolution characteristics from simpleness to complexity and from singleness to integration were present in island rural spatial functions. Meanwhile, tourism capital intervention was a key element to facilitate island rural spatial commercialization. A crucial way to avoid island rural spatial alienation and realize rural revitalization was to improve rural resilience. Overall, this study offered a model to delve into the island rural spatial evolution in China or even developing countries around the world.

Design of high-performance high-entropy nitride ceramics via machine learning-driven strategy

High-entropy nitride (HEN) ceramics have exhibited excellent mechanical properties in comparison to transition metal nitrides. However, the huge unexplored compositional space makes the trial-and-error experiments and first-principles calculations cost highly. In this work, we proposed a data augmentation generative adversarial network (DAGAN)-driven machine learning (ML) design strategy to predict the hardness, modulus and wear resistance of novel HEN compositions. Several feature selection algorithms were compared to select the optimal descriptor combination. To overcome the data shortage problem of HENs, we established a property-conditioned DAGAN and the accuracies of ML models were maximumly increased by up to 14.67%. Eight super-hard HEN systems with the hardness above 40 GPa were found among the compositional space, in which seven have yet to be experimentally synthesized. The intrinsic effects of chemical descriptors were further explored through ternary property diagrams, which provides an efficient guidance for the design of novel high-performance HENs.

High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities

The ternary ceramic MAX and recently emerging MAB phases have exhibited a combination of properties of metals and ceramics, excellent mechanical properties along with high-damage tolerance. Thermal conductivity as one of the fundamental properties is closely associated with operating conditions, which could serve as an essential indicator of thermo-functional applications. Upon performing a high-throughput density functional theory calculation on M2AX (X = B, C, or N) and M2AB2 phases in a wide compositional space, a great number of new materials are stable. Several ultra-low/high lattice thermal conductors are identified. Combined with the machine-learning, the underlying origins of approaching superior lattice thermal conductivity are unraveled and verified to be self-consistent by reverse comparison. Towards the “truly” stable materials by including reaction enthalpies, about seventy materials are retained, in which Zr2SnC and Nb2SnB having ultralow κph (< 2 W (m K)−1) are successfully synthesized and further characterized. A new continent of ceramics with superior lattice thermal conductivities is thus reported, and we tend to lay foundations of the lattice thermal conductivity of such layered ceramics by machine learning methods and physical modeling. It is believed that this work would pave the way for rational design and high-throughput studies of materials.

Accelerated design of high entropy alloys by integrating high throughput calculation and machine learning

Designing high entropy alloys (HEAs) from a large compositional space is challenging. The design process is accelerated by integrating computational techniques. The current study developed a framework for the accelerated identification of phases in HEAs by integrating high throughput calculations and machine learning (ML). The 19968 composition information was generated using high throughput CALculation of PHAse Diagrams (CALPHAD) calculations and used as a dataset for the machine learning algorithms. The Synthetic Minority Over-sampling Technique (SMOTE) makes unbiased data, and multiple machine learning algorithms are trained and compared. The Support Vector Machine (SVM) and Artificial Neural Network (NN) show test accuracy of ∼99%. The trained algorithm was used to predict phases in ternary, quaternary and quinary systems. The experimental comparison confirms that the algorithms successfully predicted the phase transitions in HEAs with respect to varying compositions. The developed framework can be adopted for designing new materials.