Composition Space Research Articles

Machine-learning-aided density functional theory calculations of stacking fault energies in steel

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

Engineering stoichiometry of high entropy fluorite oxide to obtain optimized thermophysical property and improved corrosion resistance

Engineering stoichiometry of materials is an effective strategy to optimize the comprehensive properties. Here we prepare high-entropy Er2(Y0.2Yb0.2Nb0.2Ta0.2Cex)2Oδ with single-phase fluorite structure by a solid-state reaction. The effects of stoichiometry of cerium on thermophysical properties and corrosion resistance to calcium−magnesium−alumina−silicate (CMAS) of the high-entropy oxides have been investigated. By engineering the cerium stoichiometry, thermal conductivity increases slightly from 1.21 to 1.36 W·m−1·K−1 and thermal expansion coefficients also increases from 10.56 × 10−6 to 11.61 × 10−6 °C−1 at 1000 °C. Furthermore, corrosion resistance of the high-entropy Er2(Nb0.2Ta0.2Y0.2Yb0.2Cex)2Oδ improves with the increase of cerium content. The effects of cerium content on CMAS resistance have been discussed in detail. Our work reveals that engineering stoichiometry of high-entropy oxides is expected to be an effective strategy to broaden the compositional space and optimize the thermophysical properties and CMAS corrosion resistance.

Transport properties of oil-CO2 mixtures in calcite nanopores: Physics and machine learning models

Fundamental understanding and quantitative models of the transport properties of oil-CO2 mixtures in nanopores are indispensable for physics-based models of CO2-enhanced oil recovery in unconventional oil reservoirs. This study determines the Maxwell-Stefan (M−S) diffusivities of CO2-decane (1: CO2; 2: decane /C10) mixtures in calcite nanopores with compositions relevant to CO2 Huff-n-Puff by molecular dynamics (MD) simulations. In the compositional space explored, D12 characterizing CO2-C10 interactions is relatively insensitive to composition, in contrast to that of bulk mixtures with similar compositions. D1,s characterizing CO2-wall interactions increases sharply with CO2 loading in the nanopore. In contrast, D2,s characterizing C10-wall interactions shows a nonmonotonic dependence on C10 loading. In addition, surprisingly, D2,s is negative, opposite to the expectations for dense fluid mixtures or pure decane confined in nanopores. These features of the M−S diffusivities can ultimately be traced to the fact that CO2 molecules adsorb far more strongly on pore walls than the C10 molecules, which leads to significantly heterogeneous distribution of CO2 and C10 in the nanopore and a low mobility of the adsorbed CO2 molecules. As MD simulations are computationally expensive, a non-parametric machine learning technique, called the multi-task Gaussian process regression method, is used to build a surrogate model to predict M−S diffusivities based on limited MD data. The surrogate model performs well in the compositional space it was trained with a relative root mean square error less than 10%.

Efficient property-oriented optimization of magnetic high-entropy metallic glasses via a multi-stage design strategy

High-entropy metallic glasses (HE-MGs) have drawn much attention as promising multifunctional materials combining the excellent soft magnetic properties of traditional metallic glasses and impressive mechanical properties, thermal stability, corrosion resistance, etc., of solid solution high entropy alloys. Property optimization of HE-MGs is of great significance for promoting their engineering applications. However, various constituent elements and the high chemical complexity make the possible alloying composition space extremely massive, which is very challenging for the rational design of HE-MGs. In this work, we proposed a multi-stage optimization strategy based on machine learning (ML) to accelerate the rational design of magnetic HE-MGs with desired properties. The huge composition search space was significantly narrowed by the ML-based phase prediction model and constraints from user preferences. Utility functions based on the exploitation and exploration strategy were designed to find the global optimization solutions, i.e., alloying compositions. Experiments were conducted as concept validation, and new Fe-Co-Ni-Si-B HE-MGs with balanced saturation magnetic flux density and mixing entropy were developed.

Over-Stoichiometric Metastabilization of Cation-Disordered Rock Salts.

Cation-disordered rock salts (DRXs) are well known for their potential to realize the goal of achieving scalable Ni- and Co-free high-energy-density Li-ion batteries. Unlike in most cathode materials, the disordered cation distribution may lead to more factors that control the electrochemistry of DRXs. An important variable that is not emphasized by research community is regarding whether a DRX exists in a more thermodynamically stable form or a more metastable form. Moreover, within the scope of metastable DRXs, over-stoichiometric DRXs, which allow relaxation of the site balance constraint of a rock salt structure, are particularly underexplored. In this work, these findings are reported in locating a generally applicable approach to "metastabilize" thermodynamically stable Mn-based DRXs to metastable ones by introducing Li over-stoichiometry. The over-stoichiometric metastabilization greatly stimulates more redox activities, enables better reversibility of Li deintercalation/intercalation, and changes the energy storage mechanism. The metastabilized DRXs can be transformed back to the thermodynamically stable form, which also reverts the electrochemical properties, further contrasting the two categories of DRXs. This work enriches the structural and compositional space of DRX families and adds new pathways for rationally tuning the properties of DRX cathodes.

Experimentally determined leaflet-leaflet phase diagram of an asymmetric lipid bilayer.

We have determined the partial leaflet-leaflet phase diagram of an asymmetric lipid bilayer at ambient temperature using asymmetric giant unilamellar vesicles (aGUVs). Symmetric GUVs with varying amounts of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine and DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) were hemifused to a supported lipid bilayer (SLB) composed of DOPC, resulting in lipid exchange between their outer leaflets. The GUVs and SLB contained a red and green lipid fluorophore, respectively, thus enabling the use of confocal fluorescence imaging to determine both the extent of lipid exchange (quantified for individual vesicles by the loss of red intensity and gain of green intensity) and the presence or absence of phase separation in aGUVs. Consistent with previous reports, we found that hemifusion results in large variation in outer leaflet exchange for individual GUVs, which allowed us to interrogate the phase behavior at multiple points within the asymmetric composition space of the binary mixture. When initially symmetric GUVs showed coexisting gel and fluid domains, aGUVs with less than ~50% outer leaflet exchange were also phase-separated. In contrast, aGUVs with greater than 50% outer leaflet exchange were uniform and fluid. In some cases, we also observed three coexisting bilayer-spanning phases: two registered phases and an anti-registered phase. These results suggest that a relatively large unfavorable midplane interaction between ordered and disordered phases in opposing leaflets (i.e., a midplane surface tension) can overwhelm the driving force for lateral phase separation within one of the leaflets, resulting in an asymmetric bilayer with two uniformly mixed leaflets that is poised to phase-separate upon leaflet scrambling.

Semiautomated Experiments to Accelerate the Design of Advanced Battery Materials: Combining Speed, Low Cost, and Adaptability.

A number of methodologies are currently being exploited in order to dramatically increase the composition space explored in the design of new battery materials. This is proving necessary as commercial Li-ion battery materials have become increasingly high-performing and complex. For example, commercial cathode materials have quinary compositions with a sixth element in the coating, while a very large number of contenders are still being considered for solid electrolytes, with most of the periodic table being at play. Furthermore, the promise of accelerated design by computation and machine learning (ML) are encouraging, but they both ultimately require large amounts of quality experimental data either to fill in holes left by the computations or to be used to improve the ML models. All of this leads researchers to increase experimental throughputs. This perspective focuses on semiautomated experimental approaches where automation is only utilized in key steps where absolutely necessary in order to overcome bottlenecks while minimizing costs. Such workflows are more widely accessible to research groups as compared to fully automated systems, such that the current perspective may be useful to a wide community. The most essential steps in automation are related to characterization, with X-ray diffraction being a key bottleneck. By analyzing published workflows of both semi- and fully automated workflows, it is found herein that steps handled by researchers during the synthesis are not prohibitive in terms of overall throughput and may lead to greater flexibility, making more synthesis routes possible. Examples will be provided in this perspective of workflows that have been optimized for anodes, cathodes, and electrolytes in Li batteries, the vast majority of which are also suitable for battery technologies beyond Li.

Influences of alloying composition and cooling rate on precipitation behaviors of primary Al3Zr-D023 intermetallics in Al-Mg-Li-Zr alloys

In the industrial process, the potential benefit of Al3Zr precipitates is usually weakened because coarse Al3Zr precipitates have no positive effect on the dynamic recovery, recrystallization and grain growth. Effects of alloying composition and cooling rate on precipitation and growth behaviors of primary Al3Zr-D023 intermetallics were investigated, especially in a large compositional space. Four types of elements exist in the Al3Zr-D023 phase. Increasing initial Mg, Li, Zr, and Ti contents is beneficial to increase the start precipitation temperature of Al3Zr-D023 particles. Increasing initial Mg, Li, Zr, Ti contents and reducing cooling rate are beneficial to increase the mean length, decrease the nucleation rate and the number density of Al3Zr-D023 precipitates. Furthermore, Zr and Ti element contents show a strong positive correlation with the start precipitation temperature and the final length of Al3Zr-D023 precipitates. Reducing slightly initial Ti content is beneficial to reduce the final length of Al3Zr-D023 precipitates. Furthermore, two empirical equations were obtained using the multiple linear regression method, which reveal the complex interactions rather than single-element effect of Mg, Li, Zr, and Ti.

Activation by O2 of AgxPd1-x Alloy Catalysts for Ethylene Hydrogenation.

A composition spread alloy film (CSAF) spanning all of AgxPd1-x composition space, xPd = 0 → 1, was used to study catalytic ethylene hydrogenation with and without the presence of O2 in the feed gas. High-throughput measurements of the ethylene hydrogenation activity of AgxPd1-x alloys were performed at 100 Pd compositions spanning xPd = 0 → 1. The extent of ethylene hydrogenation was measured versus xPd at reaction temperatures spanning T = 300 → 405 K and inlet hydrogen partial pressures spanning PH2in = 70 → 690 Torr. The inlet ethylene partial pressure was constant at PC2H4in = 25 Torr, and the O2 inlet partial pressure was either PO2in = 0 or 15 Torr. When PO2in = 0 Torr, only those alloys with xPd ≥ 0.90 displayed observable ethylene hydrogenation activity. As expected, the most active catalyst was pure Pd, which yielded a maximum conversion of ∼0.4 at T = 405 K and PH2in = 690 Torr. Adding a constant O2 partial pressure of PO2in = 15 Torr to the feed stream dramatically increased the catalytic activity across the CSAF at all experimental conditions and catalyst compositions without inducing catalytic ethylene combustion and without measurable O2 consumption. The presence of PO2in = 15 Torr more than doubled the maximum achievable conversion on Pd to ∼0.9 and activated alloys with as little as xPd = 0.6 for ethylene hydrogenation. Measurement of the reaction order with respect to hydrogen, nH2, showed that nH2 ≈ 0 when PO2in = 15 Torr on high xPd alloys but that nH2 increases to values between 0.5 and 1 as xPd decreases or when PO2in = 0 Torr. We attribute this PO2in-induced change in nH2 to a change in the reaction mechanism resulting from different functional catalyst surfaces: one that is O2-activated and Pd-rich and one that is Ag-capped with low activity. Both are extremely sensitive to the bulk alloy composition, xPd, and the reaction temperature, T. These results show that the activity of AgPd catalysts for ethylene hydrogenation depends strongly on the operational conditions. Furthermore, we demonstrate that the exposure of AgPd catalysts to 15 Torr of O2 at moderate temperatures leads to enhanced catalyst performance, presumably by stimulating both Pd segregation to the topmost surface and Pd activation for ethylene hydrogenation.

A systematic study on the metallophilicity of ordered five-atomic-layer MXenes using high-throughput automated workflow and machine learning

Dendrite growth is an important issue hindering practical applications of various kinds of metal (e.g. Li, Na, K, Mg, Ca, Fe, Zn, and Al) batteries. To tackle this problem, an effective method is to introduce metallophilic materials as hosts for metal anodes. Due to its high electrical conductivity, ease of assembly, and large compositional space, MXene is an excellent choice for anode host materials. However, how do MXenes’ compositions influence their metallophilicity to different kinds of metals, and how do we find MXenes with the strongest metallophilicity to different kinds of metals? To answer these questions, a density functional theory (DFT) based high-throughput automated workflow (HTAW) is designed and a machine learning (ML) study is performed focusing on the metallophilicity of ordered five-atomic-layer MXenes. For the first time, the metallophilicity of ordered five-atomic-layer MXenes from their entire compositional space to a total of eight kinds of metal anodes is studied. The metallophilicity of ∼10 % of the compositional space is investigated by the HTAW and serves as the dataset for ML study, and the remaining ∼90 % is predicted by the trained ML model. Based on the predictions, a ‘cooperation with neighbor’ mechanism governing MXenes’ metallophilicity is summarized, MXenes with the strongest metallophilicity to the eight kinds of metals are discovered, and the already synthesized -O terminated Cr2TiC2 is found to be a member of them, which represents a readily available subject for further experimental verifications and practical applications.

High‐Throughput Design of Refractory High‐Entropy Alloys: Critical Assessment of Empirical Criteria and Proposal of Novel Guidelines for Prediction of Solid Solution Stability

Refractory high‐entropy alloys (RHEAs) are a new class of metallic alloys which have been extensively studied in the past decade due to their excellent high‐temperature performances. However, the design of new lightweight and ductile RHEAs is a challenging task, since an extensive exploration of the immense compositional space of multicomponent systems is practically impossible. Aiming to reduce the experimental effort, several research groups have proposed different predictive criteria to design new high‐performing HEAs. Nevertheless, the criteria proposed so far are often based on a limited amount of data and, generally, do not differentiate between refractory and nonrefractory HEAs. To overcome these limitations, herein, a comprehensive database of properties of 265 RHEAs reported in the open literature from 2010 to 2022 is developed. Such a database is used to assess the validity of predictive empirical criteria and new guidelines for the prediction of solid solution stability in RHEAs are proposed.

Building up a view and understanding of the multifunctional activity of black phosphorous nanosheet modified with the metal atom.

Constructing metal-semiconductor interfaces by loading metal atoms onto two-dimensional material to build atomically dispersed single-atom catalysts (SACs) has emerged as a new frontier for improving atom utilization and designing multifunctional electrocatalysts. Nowadays, studies on black phosphorus nanosheets in electrocatalysis have received much attention and the successful preparation of metal nanoparticle/black phosphorus (BP) hybrid electrocatalysts indicates BP nanosheets can serve as a potential support platform for SACs. Herein, by using large-scale abinitio calculations, we explored a large composition space of SACs with transition metal atoms supported on BP monolayer (M-BP) and built a comprehensive picture of activity trend, stability, and electronic origin towards oxygen reduction and evolution reaction (ORR and OER) and hydrogen evolution reaction (HER). The results show that the catalytic activity can be widely tuned by reasonable regulation of metal atoms. Ni-, Pd-, and Pt-BP could effectively balance the binding strength of the target intermediates, thus achieving efficient bifunctional activity for OER and ORR. Favorable bifunctional catalytic performance for OER and HER can be realized on Rh-BP. Especially, Pt-BP exhibits promising trifunctional activity towards OER, ORR, and HER. Multiple-level corrections among overpotential, Gibbs free energy, orbital population, and d-band center reveal that the trend and origin of catalytic activity are intrinsically determined by the d-band center of metal sites. The thermodynamic and dynamic stability simulations demonstrate that the active metal centers are firmly anchored on BP substrate with intact M-P bonds. These findings provide a theoretical basis for the rational design of BP-based SACs toward promising multifunctional activity.

Fe Site Order and Magnetic Properties of Fe1/4NbS2.

Transition-metal dichalcogenides (TMDs) have long been attractive to researchers for their diverse properties and high degree of tunability. Most recently, interest in magnetically intercalated TMDs has resurged due to their potential applications in spintronic devices. While certain compositions featuring the absence of inversion symmetry such as Fe1/3NbS2 and Cr1/3NbS2 have garnered the most attention, the diverse compositional space afforded through the host matrix composition as well as intercalant identity and concentration is large and remains relatively underexplored. Here, we report the magnetic ground state of Fe1/4NbS2 that was determined from low-temperature neutron powder diffraction as an A-type antiferromagnet. Despite the presence of overall inversion symmetry, the pristine compound manifests spin polarization induced by the antiferromagnetic order at generic k points, based on density functional theory band-structure calculations. Furthermore, by combining synchrotron diffraction, pair distribution function, and magnetic susceptibility measurements, we find that the magnetic properties of Fe1/4NbS2 are sensitive to the Fe site order, which can be tuned via electrochemical lithiation and thermal history.

Local Search and the Evolution of World Models.

An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily incremental, involving the generation and selection among random local mutations and recombinations of (parts of) one's current model. We argue that, by narrowing and guiding exploration, this feature of cognitive search is what allows human learners to discover better theories, without ever grappling directly with the problem of finding a "global optimum," or best possible world model. We suggest this aspect of cognitive processing works analogously to how blind variation and selection mechanisms drive biological evolution. We propose algorithms developed for program synthesis provide candidate mechanisms for how human minds might achieve this. We discuss objections and implications of this perspective, finally suggesting that a better process-level understanding of how humans incrementally explore compositional theory spaces can shed light on how we think, and provide explanatory traction on fundamental cognitive biases, including anchoring, probability matching, and confirmationbias.

Rapid design and screen high strength U-based high-entropy alloys from first-principles calculations

Reducing the exploration of multi-principal element alloy space is a key challenge to design high-performance U-based high-entropy alloy (UHEA). Here, the best combination of multi-principal element can be efficiently acquired because proposed alloying strategy and screening criteria can substantially reduce the space of alloy and thus accelerate alloy design, rather than enormous random combinations through a trial-and-error approach. To choose the best seed alloy and suitable dopants, the screening criteria include small anisotropy, high specific modulus, high dynamical stability, and high ductility. We therefore find a shortcut to design UHEA from typical binary (UTi and UNb) to ternary (UTiNb), quaternary (UTiNbTa), and quinary (UTiNbTaFe). Finally, we find a best bcc senary UHEA (UTiNbTaFeMo), which has highest hardness and yield strength, while maintains good ductility among all the candidates. Compared to overestimation from empirical strength-hardness relationship, improved strength prediction can be achieved using a parameter-free theory considering volume mismatch and temperature effect on yield strength. This finding indicates that larger volume mismatch corresponds to higher yield strength, agreeing with the available measurements. Moreover, the dynamical stability and mechanical properties of candidates are greatly enhanced with increasing the number of multi-principal element, indicating the feasibility and effectiveness of adopted alloying strategy. The increasing of multi-principal element corresponds to the increasing valence electron concentration (VEC). Alternatively, the mechanical properties significantly improve as increasing VEC, agreeing with measurements for other various bcc HEAs. This work can speed up research and development of advanced UHEA by greatly reducing the space of alloy composition.

A predictive approach for the compositional and temperature representation of thermal conductivity in multicomponent molten salt systems for advanced energy applications

This study presents a theoretical model, based on kinetic theory, for accurately predicting thermal conductivity in molten salt mixtures, a vital factor in designing advanced energy systems such as concentrating solar power, thermal energy storage, and advanced nuclear reactors. Applicable to non-complex, non-reciprocal systems, the model represents thermal conductivity with respect to composition and temperature. We evaluated the model using molecular dynamics simulations on binary systems from fluoride, chloride, bromide, iodide salt families. The model predictions were also compared against existing experimental datasets for those families as well as binary nitrates and carbonates. The model demonstrated an improvement in predictive accuracy over the ideal linear estimation and showed reasonable agreement with the reliable experimental data. Deviations from ideal conductivity, analyzed using the Redlich-Kister method, pointed to the molecular weight and thermal conductivity of individual compounds as primary influencing factors. When applied to predict the thermal conductivity of higher order eutectic salt mixtures relevant to energy applications, the model showed predictive accuracy comparable to empirical methods. The model was further applied to generate compositional mapping to quantify deviations from ideality over the compositional space. This advance enhances new molten salt mixtures evaluation efficiency and extends the model to complex mixtures.

EVALUATION OF MACHINE LEARNING ASSISTED PHASE BEHAVIOR MODELLING OF CO2-OIL SYSTEMS

This study evaluates machine learning (ML) methodologies in the pursuit of advancing thermodynamic flash calculations that are vital for carbon dioxide storage applications and for the oil and gas industry in general. We generated a dataset for training machine learning algorithms using a traditional physics-based model. This developed hybrid model incorporated into the machine learning model underlying physical constraints. While preliminary results from training and numerical matching were promising, the hybrid model's real-world application revealed non-trivial shortcomings. Specifically, mismatch in the multiphase region was observed during compositional space testing. Such subtle but significant flaws in machine learning methods have profound implications for the accurate physics of carbon storage projects. This article, therefore, presents advantages and disadvantages of employing ML for thermodynamic calculations, emphasizing the intricate balance between computational efficiency and representative physics.

Selectivity of β-Cyclodextrin Polymer toward Aquatic Contaminants: Insights from Ultrahigh-Resolution Mass Spectrometry of Dissolved Organic Matter.

Selectivity in solid-phase extraction (SPE) materials has become increasingly important for analyte enrichment in sensitive analytical workflows to alleviate detrimental matrix effects. Molecular-level investigation of matrix constituents, which are preferentially extracted or excluded, can provide the analytical chemist with valuable information to learn about their control on sorbent selectivity. In this work, we employ nontargeted Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) to elucidate the molecular chemodiversity of freshwater-derived dissolved organic matter (DOM) extracted by the selective model sorbent β-cyclodextrin polymer (β-CDP) in comparison to conventional, universal SPE sorbents (i.e., Oasis HLB, Supel-Select HLB, and LiChrolut EN). Statistical analysis of MS data corroborated the highly selective nature of β-CDP by revealing the extracted DOM spectra that are most dissimilar to original compositions. We found that its selectivity was characterized by pronounced discrimination against highly oxygenated and unsaturated DOM compounds, which were associated with the classes of lignin-like, tannin-like, and carboxylic-rich alicyclic molecules. In contrast, conventional sorbents excluded less highly oxygenated compounds and showed a more universal extraction behavior for a wide range of DOM compositional space. We lay these findings in a larger context that aids the analyst in obtaining an a priori estimate of sorbent selectivity toward any target analyte of interest serving thereby an optimization of sample preparation. This study highlights the great value of nontargeted ultrahigh-resolution MS for better understanding of targeted analytics and provides new insights into the selective sorption behavior of novel sorbents.

A novel mixing rule model to predict the flowability of directly compressed pharmaceutical blends

In the pharmaceutical industry, powder flowability is an essential manufacturability attribute to consider when selecting the suitable manufacturing route and formulation. The selection of the formulation is usually based on the physical and chemical properties of the Active Pharmaceutical Ingredient (API) under consideration. Current industrial practice heavily relies on experimental work, which often results in significant labor and API consumption that results in higher costs. In this study we describe the development of a mixing rule to predict powder blend flowability from the flow properties of the individual components for industrial formulations manufactured via Direct Compression (DC). The mixing rule assumes that the granular solids’ interactions are dominated by cohesive forces but are pragmatic to calibrate from the perspective of the typical data collated in an industrial environment. The proposed model was validated using a range of different APIs and the results show that the model can effectively predict the flowability properties of any formulation across the space of DC-relevant formulation compositions. Finally, a connection between the model and APIs properties (shape and size) was investigated via a linear correlation between the API particle properties and interparticle forces.

Computational and experimental investigation of a new non equiatomic FCC single-phase Cr15Cu5Fe20Mn25Ni35 high-entropy alloy

The structural, mechanical, and thermal properties of a new non-equiatomic face-centered cubic (FCC) structured high entropy alloy (HEA) were examined computationally using density functional theory (DFT) calculations and validated experimentally. To identify potential single phase HEA candidates, the composition space of the CrCuFeMnNi alloy system, including 1451 alloys, was investigated using the CALculation of PHAse Diagrams (CALPHAD) method and Thermo-Calc software. The CALPHAD approach successfully predicted an FCC single-phase Cr15Cu5Fe20Mn25Ni35 alloy. Elastic constants and thermal properties of the Cr15Cu5Fe20Mn25Ni35 HEA were determined through DFT calculations using a 100-atom supercell. To validate the DFT calculations, HEA samples were prepared by the arc melting technique, and X-ray diffraction (XRD) analysis confirmed a single-phase FCC structure.The DFT calculated elastic constants values satisfied the Born stability conditions for stability of a cubic crystal structure. Pugh's theory indicated that this alloy exhibits ductility and metallic behavior due to its B/G ratio exceeding 1.75 and positive Cauchy pressure. Additionally, Poisson's ratio exceeded 0.26, further confirming its ductile nature. Experimental tests confirmed these findings by measuring bulk modulus, shear modulus, Young's modulus, and Poisson's ratio. The calculated CV values showed good agreement with experimental data at different temperatures. The linear coefficient of thermal expansion increased rapidly at lower temperatures before becoming linear. Both XRD as well as DFT calculations yielded similar lattice constant results for this HEA. These findings provide insights into various properties of the Cr15Cu5Fe20Mn25Ni35 HEA, including its structural stability, mechanical behavior and thermal characteristics.