Composition Space Research Articles

Inverse catalysts: tuning the composition and structure of oxide clusters through the metal support

Computational modeling of metal–oxide interfaces is challenging due to the large search space of compositions and structures and the complexity of catalyst materials under operating conditions in general. In this work, we develop an efficient structure search workflow to discover chemically unique and relevant nanocluster geometries of inverse catalysts and apply it to ZnyOx and InyOx on Cu(111), Pd(111), and Au(111). We show that the workflow is successful in obtaining a large range of chemically distinct structures. Structural geometry trends are identified, including stable motifs such as tripod, rhombus, and pyramidal motifs. Using ab initio thermodynamics, we explore the in situ stability of the structures, including single-atom alloys, at a range of oxygen availabilities. This approach allows us to find trends such as the susceptibility to oxidation of the different systems and the range of stability of different cluster motifs. Our analysis highlights the importance of taking the diversity of sites exposed by metal–oxide interfaces into account in catalyst design studies.

Exploring composition space by Nb and Sn substitution, microstructure and Seebeck behaviour in Zr2FeNiSb2 double half-Heusler compound

Exploring composition space by Nb and Sn substitution, microstructure and Seebeck behaviour in Zr2FeNiSb2 double half-Heusler compound

Discovering virtual Na-based argyrodites as solid-state electrolytes using DFT, AIMD, and machine learning techniques

In light of the current absence of experimentally synthesized Na-based argyrodites, we systematically investigated a large compositional space comprising 4375 hypothetical Na-based argyrodite structures through DFT and AIMD calculations.

High-Throughput Exploration of Ti-V-Nb-Mo Carbide MXenes Using Neural Network Potentials and Their Evaluation as Catalysts for Hydrogen Evolution Reaction.

Realization of a sustainable hydrogen economy in the future requires the development of efficient and cost-effective catalysts for its production at scale. MXenes (Mn+1Xn) are a class of 2D materials with 'n' layers of carbon or nitrogen (X) interleaved by 'n+1' layers of transition metal (M) and have emerged as promising materials for various applications including catalysts for hydrogen evolution reaction (HER). Their properties are intimately related to both their composition and their atomic structure. Recently, high entropy MXenes were synthesized, opening a vast compositional space of potentially stable and functionally superior materials. Detailed atomistic modeling enables us to systematically explore this extensive design space, which is otherwise infeasible in experiments. We have developed a Neural Network Potential (NNP) to model (TixVyNbzMop)n+1Cn MXenes (x+y+z+p = 1; n = 1,2,3) by training against Density Functional Theory (DFT) data in an active learning fashion. We then used the developed NNP to perform hybrid Monte Carlo-Molecular Dynamics (MC-MD) simulations to identify thermodynamically stable compositions and investigate the relative arrangement of transition metal atoms within and across layers. Thermodynamic stability increased with Mo content and its presence on the surface layer. We further investigated the catalytic performance of stable MXenes for the HER and observed that the center of the oxygen p-band (εp) correlated well with the energy of adsorption of a hydrogen atom ΔG(*H). Subsurface metal atoms significantly influenced the ΔG(*H) values at the surface via both ligand and strain effects. Our work expands the space of potentially stable MXene compositions, providing targets for synthesis and their evaluation in various applications.

Machine Learning Advances in High-Entropy Alloys: A Mini-Review.

The efficacy of machine learning has increased exponentially over the past decade. The utilization of machine learning to predict and design materials has become a pivotal tool for accelerating materials development. High-entropy alloys are particularly intriguing candidates for exemplifying the potency of machine learning due to their superior mechanical properties, vast compositional space, and intricate chemical interactions. This review examines the general process of developing machine learning models. The advances and new algorithms of machine learning in the field of high-entropy alloys are presented in each part of the process. These advances are based on both improvements in computer algorithms and physical representations that focus on the unique ordering properties of high-entropy alloys. We also show the results of generative models, data augmentation, and transfer learning in high-entropy alloys and conclude with a summary of the challenges still faced in machine learning high-entropy alloys today.

A Universal Solid-Phase Synthetic Strategy for Ultrafine Intermetallic Libraries Confined in Ordered Mesoporous Carbon.

Ordered intermetallic nanocatalysts supported on high-surface-area skeletons are of great importance in catalysis and have disclosed notable catalytic activity and stability that are remarkably better than their random alloy counterparts. Ultrafine intermetallic nanocatalysts are synthetically challenging, especially for universal and scaled-up synthesis, because of inevitable sintering and phase separation under high temperatures that promote atomic alloying and ordering. Herein, a universal solid-phase and scaled-up method is reported for synthesizing ultrafine intermetallic nanocatalysts with uniform size distributions and wide compositional spaces confined in ordered mesoporous carbon (OMC) supports, where the strong physical confinement and chemical interaction between metals and sulfur/mesoporous templates remarkably suppress the high-temperature sintering and phase separation even up to 1000°C. Libraries of intermetallic nanocatalysts are successfully synthesized including 52 combinations of host platinum/palladium/rhodium with 15 guest elements confined in 4 OMC supports. Taking oxygen reduction and hydrogen evolution reactions as examples, the intermetallic PtFe nanocatalysts hold remarkable performance, whose activities reach up to ten times higher than commercial Pt/C and also are comparable to the best electrocatalysts reported recently. This feasible synthetic strategy offers an intermetallic library spanning from binary to senary materials for industrial synthesis and applications.

Data-Efficient Multifidelity Training for High-Fidelity Machine Learning Interatomic Potentials.

Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with reduced computational costs. However, the high cost of assembling high-fidelity databases hampers the application of MLIPs to systems that require high chemical accuracy. Utilizing an equivariant graph neural network, we present an MLIP framework that trains on multifidelity databases simultaneously. This approach enables the accurate learning of high-fidelity PES with minimal high-fidelity data. Employing the generalized gradient approximation (GGA) and meta-GGA as low- and high-fidelity approaches, respectively, we tested this framework on the Li6PS5Cl and InxGa1-xN systems. The results show that using a high-fidelity training set with a size approximately 10% of the low-fidelity set, the multifidelity training framework achieves excellent accuracy, with Li-ion conductivity predictions within 10% error and InxGa1-xN mixing energy showing an R2 of 0.98 compared to the reference high-fidelity MLIP results. It indicates that geometric and compositional spaces not covered by the high-fidelity meta-GGA database can be effectively inferred from low-fidelity GGA data, thus enhancing accuracy and molecular dynamics stability. We also developed a general-purpose MLIP that utilizes both GGA and meta-GGA data from the Materials Project, significantly enhancing MLIP performance for high-accuracy tasks such as predicting energies above hull for crystals in general. Furthermore, we demonstrate that the present multifidelity learning is more effective than transfer learning or Δ-learning and that it can also be applied to learn higher-fidelity up to the coupled-cluster level. We believe this methodology holds promise for creating highly accurate bespoke or universal MLIPs by effectively expanding the high-fidelity data set.

Features of isolation and nature of low-resistivity oil-saturated reservoirs of the Middle Jurassic deposits of the Akshabulak Central field of the South Torgai oil and gas basin

At the current stage of development of the oil and gas industry of our country, additional exploration of existing fields, understudied promising areas, identification of missed horizons, methods of evaluation and development of non-standard reservoirs are of particular relevance. Reservoirs with low specific electrical resistivity can be referred to non-standard reservoirs, and there are some difficulties in assessing hydrocarbon prospects of these reservoirs. Low resistivity reservoirs can be productive reservoirs with high residual water saturation as well as reservoirs for which generally accepted interpretation techniques are ineffective. Proper analysis of the reasons that lead to underestimation of the resistivity of productive reservoirs allows choosing the most effective interpretation methods. The article is devoted to the study of the features of reservoirs with low electrical resistivity, their nature and role in the process of fluid accumulation. The basic methods of identification of low resistivity zones in reservoir rocks, their physical and chemical characteristics that cause low resistivity, including mineralogical composition, saturation, porosity, permeability and pore space structure, are considered, and their influence on the filtration-capacitance characteristics is analyzed. Approaches to integrating data from various methods (geophysical and geological-technological studies, laboratory measurements) are described. Special attention is paid to the influence of low resistivity on the interpretation of data from geophysical methods. The results of the study have significant practical potential for optimization of field development.

Predicting Mechanical and Thermal Properties of High‐Entropy Ceramics via Transferable Machine‐Learning‐Potential‐Based Molecular Dynamics

AbstractThe mechanical and thermal performance of high‐entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high‐entropy ceramics significantly hinders their development with desired mechanical and thermal properties. Herein, taking high‐entropy carbides (HECs) as the model, the efficiency and effectiveness of predicting mechanical and thermal properties via transferable machine‐learning‐potential‐based molecular dynamics (MD) have been demonstrated. Specifically, a transferable neuroevolution potential (NEP) with broad compositional applicability for HECs of ten transition metal elements from group IIIB‐VIB is efficiently constructed from the small dataset comprising unary and binary carbides with an equal amount of ergodic chemical compositions. Based on this well‐established transferable NEP, MD predictions on mechanical and thermal properties of different HECs have shown good agreement with the results of first‐principles calculations and experimental measurements, validating the accuracy, transferability, and reliability of using the transferable machine‐learning‐potential‐based MD simulations in investigating mechanical and thermal performance of HECs. This work provides a strategy to accelerate the search for high‐entropy ceramics with desirable mechanical and thermal properties.

Heterostructure Engineering in High‐Entropy Alloy Catalysts

ABSTRACTConfronting the limitation of traditional homogeneous high‐entropy alloys (HEAs) with randomly distributed elements and active sites, heterostructured HEAs were developed to further amplify catalytic activity and stability. This perspective dissects the genesis of heterogeneity within HEAs, highlighting how their expansive compositional space facilitates the customization of heterogeneity. By manipulating key factors, such as chemical affinity, standard redox potentials, and oxidation potential, researchers are tapping into heterostructured HEAs with unprecedented attributes. Strategies like acid leaching, galvanic replacement, and additive deposition are broadening the structural repertoire of HEAs, steering the development of heterostructured catalysts. This perspective synthesizes current discoveries, introduces provocative concepts, and provides a roadmap for structural engineering in HEA catalysts, particularly harnessing the heterogeneity of HEAs to elevate their catalytic efficiency. The confluence of theoretical and practical advancements is anticipated to lead the way in the evolution of HEA catalysts, endowing them with exceptional capabilities.

Interpolation of compound semiconductor alloy parameters from those of their constituents

Several methods have been proposed for interpolation of the value of physical parameters of quaternary alloys from those of their constituent ternary and binary sub-alloys. These expressions agree when non-linear bowing terms are not required; they differ in how the bowing terms of the bounding ternaries should be utilized. Common interpolation expressions for quaternaries can be generalized into two groups: (1) those that use a linear interpolation of the nearest ternary parameter values and (2) those that interpolate over binary values with a bowing term derived from the bounding ternaries. The second group of methods is equivalent to a polynomial expansion over the alloy’s interpolation space. For compound semiconductor alloys, the geometry of the composition space is the direct sum of the group-III and group-V mixture sub-spaces. The mixture sub-spaces are best described using barycentric coordinates on a regular simplex. A general polynomial expansion of the value of an alloy parameter using barycentric coordinates for the group-III and group-V simplex spaces is described along with an algorithm to generate interpolation expressions for alloys with arbitrary numbers of elements, including quinary and senary alloys. It is shown that a polynomial expansion produces values in closer agreement with the direct gap of quaternaries lattice-matched to common substrates than do approaches using an interpolation of the ternary values, despite a prominent recommendation to the contrary. Finally, a quaternary correction term is described that improves the predicted direct bandgap energies of GaInAsSb for compositions near those lattice matched to InP, InAs, and GaSb.

Integrated design of aluminum-enriched high-entropy refractory B2 alloys with synergy of high strength and ductility.

Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.

Physics-coupled Data-driven Design of High-Temperature Alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. We anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

A Phase-State-Radius-Based Multiphase-State Identification Acceleration Approach of the Hydrocarbons-CO2-H2O System for Compositional Reservoir Simulation

Summary The CO2-enhanced oil recovery (CO2-EOR) technique could increase the oil recovery factor and achieve subsurface CO2 storage. To evaluate the feasibility of CO2-EOR operation, an equation-of-state-based (EOS-based) compositional simulation is inevitable. However, time-consuming multiphase flash calculations limit the application of the conventional EOS-based compositional simulation in large-scale reservoir simulations. Therefore, there is an urgent need to accelerate the EOS-based compositional simulation by reducing the time spent on the multiphase flash calculations. In this study, we develop a fast, multiphase-state identification approach to achieve high computational efficiency of the compositional simulation. First, taking one given point in the compositional space composed by pressure and the overall composition as the original point, the phase-state radius (PSR) is defined as a radius inside which all points have the same phase state as the given point. Subsequently, the results of the multiphase flash calculations of the points in the compositional space are stored. Finally, when a new point is inside the PSR of one calculated point, the stored results are used to accelerate the new multiphase flash calculations of the new point by omitting the phase stability analysis (PSA) and possible additional multiphase split (MS) calculations. Case studies show a 6–10% reduction in multiphase flash computation time compared with two existing methods when the fluid is far from the phase boundary and a reduction of at least 17.5% when the fluid is near the phase boundary.

How to Produce Cultural Space for Sustainable Development Towards Rural Revitalization: A Case Study of China

Cultural revitalization is a key factor in the sustainable development of the countryside and is included in the five-dimensional indicators of China's rural revitalization strategy. Although established studies have examined the spatial dimension of rural cultural revitalization, the internal structure of spatial production and the logic of action are still not entirely clear. Based on an interview survey in villages in northern Henan, China, this paper conducts a qualitative study of the structured characteristics of rural cultural spatial production and the actor logic behind it using a grounded theory research method. The results show that the production of local cultural space, socio-cultural space, mass recreational space, and cultural industries space constitute a composite structured rural cultural space. Public power, intellectual elites, residents' rights and industrial capital have demonstrated a “co-constructive” cooperative relationship This paper provides a theoretical and empirical basis for promoting rural cultural revitalization and sustainable development through the production of rural cultural space.

Hydrated silicate ionic liquids: Ionic liquids for silicate material synthesis

The development of Hydrated Silicate Ionic Liquids, which are hypo-hydrated room temperature melts of alkali silicates, has created new opportunities for synthesizing porous silicate materials. Their discovery also allows to reinterpret the role of an ionic liquid for zeolite synthesis and offers unique opportunities to study crystallization in situ. The reduced complexity of HSILs together with a large space of achievable zeolitic phases and framework compositions, renders them excellent systems for modelling and computer simulations. This work summarizes the impact of alkalinity, hydration, and cation type on silicate speciation and provides physicochemical data spanning relevant temperatures and compositions for zeolite formation. The data is meant as a reference point to further accommodate and verify (computational) models.

Can Hume-Rothery rules predict single-phase high-entropy rocksalt oxide phases?

ABSTRACT In the quest for understanding and tailoring the formation and properties of entropically stabilised ceramics, rocksalt-structured (MgNiCoCuZn)O occupies a central place. To date, most of the reported high-entropy rocksalt oxides (HERSOs) are derivatives of the original (MgNiCoCuZn)O, and only a couple of other novel HERSOs have been synthesised. To pave the way for the discovery of new HERSOs, we seek rapid and effective methodologies for screening large compositional spaces such as those associate with high entropy oxides. In this letter, we analyse modifications of the Hume-Rothery (HR) rules that govern the formation of solid-solution metallic alloys. We propose that HERSOs can form based on maintaining an oxidation state of +2 for all cations and a low value for the coefficient of variation associated with the lattice constants of the unmixed constituent oxides in a (hypothetical or actual) rocksalt phase. As a result of applying Hume-Rothery-inspired rules, we provide an heuristic explanation for the synthesizability of three experimentally realised HERSOs, and list a significant number of other HERSO compositions that can potentially be realised via similar methodologies. The rules devised in this work for HERSOs should provide guidance for future synthesis efforts, and we expect that community use could lead to further refinements as well as to modifications for application-specific screening.

Unraveling symmetric hierarchy in solid-state reactions of tungsten-based refractory metal carbides through first-principles calculations

Refractory metal carbides, usually fabricated via solid state reactions, require precise control of their reactants and temperature, especially when they enter a complex compositional space, like high-entropy (multi-component) carbides. In this work, the solid-state reactions of tungsten based refractory metal carbides M-W-C (M = Ti, Zr, Hf, V, Nb, Ta) are systematically studied through first-principles thermodynamic calculations and percolation simulations, and its relationship with symmetric principles is unraveled. Symmetric hierarchy is defined by the group-subgroup Bärnighausen tree and the gap in their space group number. It suggests MC and WC/W2C are two possible reactants to form the highest symmetric M-W-C ternary carbides, and indicates the larger the gap in their space group number, the harder the reactions. From the symmetric hierarchy, we found the reaction path from MC to M-W-C ternary carbides is the most probable, supported by the Gibbs reaction free energy. Carbon percolation within the metal framework plays another role in the solid-state reactions of tungsten based refractory metal carbides. It reveals the phase transition from M6W6C to M3W3C undergoes a transient M2W2C. The success in predicting the phase relationship of M-W-C ternary system offers a new paradigm for the design and synthesis of high-entropy carbides, nitrides, and oxides.

Machine Learning-Guided Discovery of PGM-Free Oxyphosphide Oxygen Evolution Catalysts for Alkaline Water Electrolysis

The sluggish kinetics of the oxygen evolution reaction (OER) create a bottleneck for many sustainable energy technologies, including water electrolysis, metal-air batteries, and electrochemical CO2 reduction reaction (CO2RR). While platinum group metal (PGM) catalysts like IrO2 and RuO2 exhibit high OER activity in proton exchange membrane water electrolysis (PEMWE), their cost hinders widespread adoption. Anion exchange membrane water electrolysis (AEMWE) emerges as a promising technology with the potential to be both cost-effective and sustainable for hydrogen production. It relies on PGM-free OER catalysts, bridging the gap and merging the strengths of PEM and traditional alkaline electrolysis systems. Transition metal (TM) oxides, layered double hydroxides, and oxyphosphides, particularly in alkaline media, have emerged as promising OER catalysts due to their high activity and durability. However, the vast chemical space of oxyphosphide compositions makes traditional trial-and-error approaches to catalyst discovery inefficient. This presentation explores the application of machine learning (ML) to accelerate the discovery of novel PGM-free oxyphosphide OER catalysts for alkaline water electrolysis.Recent advancements in active learning, a powerful branch of machine learning, have demonstrated efficient and effective explorations of materials synthesis space, particularly for electrocatalytic applications. For instance, active learning has been used to optimize synthesis conditions for oxygen reduction reaction (ORR) catalysts, leading to a 33% improvement in activity compared to initial samples [1-4]. This presentation proposes a similar active learning strategy to explore and optimize the synthesis space for PGM-free oxyphosphide OER catalysts.This presentation will detail the following: Challenges of Traditional ApproachesActive Learning for OER Catalyst DesignMachine Learning and High-Throughput Synthesis This combined approach offers a powerful strategy for the rapid discovery of efficient and cost-effective PGM-free oxyphosphide OER catalysts, ultimately accelerating advancements in sustainable water electrolysis technologies.AcknowledgmentsThis work was supported by the U.S. Department of Energy (DOE), Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office (HFTO) under the auspices of the Electrocatalysis Consortium (ElectroCat 2.0). Argonne is managed for the U.S Department of Energy by the University of Chicago Argonne, LLC, under Contract DE-AC-02-06CH11357. Kort-Kamp WJ, Ferrandon M, Wang X, Park JH, Malla RK, Ahmed T, Holby EF, Myers DJ, Zelenay P. Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts. Journal of Power Sources. 2023; 559, 232583.Khalafallah D, Farghaly AA, Ouyang C, Huang W, Hong Z. Atomically dispersed Pt single sites and nanoengineered structural defects enable a high electrocatalytic activity and durability for hydrogen evolution reaction and overall urea electrolysis. Journal of Power Sources. 2023, 558, 232563.Onajah S, Sarkar R, Islam MS, Lalley M, Khan K, Demir M, Abdelhamid HN, Farghaly AA. Silica‐Derived Nanostructured Electrode Materials for ORR, OER, HER, CO2RR Electrocatalysis, and Energy Storage Applications: A Review. The Chemical Record. 2024, e202300234.Sarkar R, Farghaly AA, Arachchige IU. Oxidative Self-Assembly of Au/Ag/Pt Alloy Nanoparticles into High-Surface Area, Mesoporous, and Conductive Aerogels for Methanol Electro-oxidation. Chemistry of Materials. 2022, 34(13), 5874-87.

Electrodeposition for the Guided Synthesis and Screening of Binary Alloy Thin Films

Alloy coatings can enhance the mechanical and electrochemical properties of engineering materials such as surface hardness, wear resistance, catalytic performance, and corrosion resistance. The development and testing of alloy films requires extensive time and experimentation due to the non-monotonic variation of many properties with alloy composition and the large experimental space of compositions, which increases rapidly with the number of components in the alloy. The development time of alloy thin films can be reduced by analyzing many compositions in a single experiment. However, many current thin film deposition-based gradient synthesis techniques are expensive, produce a limited experimental range of compositions, and may not vary the composition of each component independently. Electrodeposition is an attractive technique for thin film deposition due to its low cost, high scalability, and sensitivity to the geometry of the deposition environment, but conventional co-electrodeposition of multiple metals from a single electrolyte bath suffers from limited compositional control and experimental range. We report an electrodeposition-based method that produces a wide range of compositions on a single sample with good compositional control. To demonstrate, we coated samples of polished Cu with NiCo alloys with gradient local composition. The composition, phase, morphology, and performance of the NiCo thin films are investigated using a range of high-resolution techniques including x-ray fluorescence spectroscopy, synchrotron X-ray diffraction, SEM, and nanoindentation.