AI Design for High Entropy Alloys: Progress, Challenges and Future Prospects

Abstract

High-entropy alloys have demonstrated significant application potential in many industrial fields due to their outstanding comprehensive properties. However, the complex multi-component compositions pose challenges for traditional design approaches. In recent years, artificial intelligence (AI) technology, with its powerful capabilities in data analysis, prediction, and optimization, has provided new pathways for rapid discovery and performance modulation of high-entropy alloys. This paper systematically reviews the latest advancements in AI applications for high-entropy alloy design, covering key technologies such as machine learning models (e.g., active learning, generative models, transfer learning), high-throughput computing and experimental data processing, phase structure and property prediction. It also presents typical application cases, including compositional optimization, phase structure prediction, performance synergistic regulation, and novel material discovery. Although AI has significantly improved design efficiency and accuracy, challenges remain, such as the scarcity of high-quality data, insufficient model interpretability, and interdisciplinary integration. Future efforts should focus on building a more robust data ecosystem, enhancing model transparency, and strengthening closed-loop validation between AI and experimental science to advance intelligent design and engineering applications of high-entropy alloys.

1. Introduction

1.1. High Entropy Alloys

High Entropy Alloys (HEAs) are novel multi-component materials composed of five or more elements in stoichiometric or near-stoichiometric ratios [1], characterized by high mixed entropy [2,3]. Mixing entropy (ΔS_mix) refers to the increase in system entropy when different substances are mixed, reflecting an increase in microscopic disorder. It includes compositional complexity and configurational entropy (ΔS_conf). Compositional complexity (n): Geometric and electronic complexity caused by differences in element count, atomic size, electronegativity, etc., leading to localized chemical heterogeneity. Configurational entropy (ΔS_conf): Ideal mixing entropy $\Delta \mathrm{S}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{f}=-\mathrm{R}\sum _{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}\ln {\mathrm{x}}_{\mathrm{i}}$, which is only related to molar fraction and not element type differences. For an ideal system, the formula is: $\Delta \mathrm{S}\mathrm{m}\mathrm{i}\mathrm{x}=-\mathrm{n}\mathrm{R}\sum _{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}\ln {\mathrm{x}}_{\mathrm{i}}$, where xi represents the molar fraction of component i. Mixing entropy is always positive; the more homogeneous the mixture, the greater the entropy increase, as shown in Figure 1. High mixing entropy is only one of the necessary thermodynamic conditions for the formation of a single-phase solid solution, and its realization depends on enthalpy-entropy competition, dynamic freezing and lattice distortion. This entropy-plus-kinetics synergy stabilizes single solid solution phase structures [4,5] and enhances mechanical properties, corrosion resistance, high-temperature stability, and controllable performance through mechanisms including hysteresis diffusion, lattice distortion, and the “cocktail effect” [6,7], as shown in Figure 2. Breaking away from traditional alloy design paradigms, these materials have found extensive applications in energy systems, catalysis, and high-temperature structural engineering, demonstrating significant practical potential and research value [5,8,9].

1.2. The Thermodynamic-Dynamic Controversy

Notably, the current academic debate centers on whether the formation of high-entropy single phases is a thermodynamically entropy-driven equilibrium or dynamically trapped metastable states. Early “entropy-stability” models assumed ΔG = ΔH − TΔS < 0 to stabilize single phases. However, many high-entropy materials are fabricated through high-temperature sintering or rapid quenching. During these non-equilibrium processes, systems may fail to reach thermodynamic equilibrium and instead be “frozen” in a high-entropy single phase. This state remains metastable at room temperature (ΔG > 0), but phase transitions are dynamically suppressed due to extremely slow atomic diffusion rates (“diffusion barrier effect”), thus preserving it. Research by Miao et al. [11] demonstrates that the complex interplay between configurational entropy, thermodynamics, and kinetics significantly influences nanostructures and chemical environments in high-entropy oxide (HEO) films. Sharma et al. [12] found that the presence of multiple principal elements accelerates amorphization through severe lattice distortion and increased configurational entropy. The atomic size differences of hetero-elements in alloys ensure high stability of formed amorphous phases, effectively preventing transition to ordered lattice structures even after annealing. Therefore, “high-entropy single phases” are not purely thermodynamically inevitable but result from a tripartite interplay of entropy, enthalpy, and kinetics. Therefore, this paper argues that both “thermodynamic descriptors” (ΔH_mix, Ω parameters) and “dynamic descriptors” (diffusion activation energy Q, cold velocity) should be introduced into the AI model, rather than just ΔS_conf.

1.3. Material Design

Current design approaches for high-entropy materials primarily include stoichiometric ratio design, non-stoichiometric ratio design, computational aid design, phase diagram calculations (CALPHAD), experimental screening, gradient material design, and nanostructure design [13,14,15]. Stoichiometric ratio design is straightforward and suitable for initial screening, though it may overlook the unique roles of certain elements [16]. Non-stoichiometric ratio design allows flexible performance tuning but involves higher complexity [17,18]. Computational aid design significantly accelerates material development while reducing experimental costs, though it heavily relies on model accuracy [19,20,21]. Phase diagram calculations provide phase stability and transition information, yet depend on database precision [22,23,24]. Experimental screening yields direct, reliable results but is time-consuming and labor-intensive [25,26,27]. Gradient material design and nanostructure design enable multifunctional integration and performance enhancement, though they require complex manufacturing processes [28,29,30]. In practice, these methods each have distinct advantages and disadvantages, and multiple approaches are often combined to optimize material properties.

As shown in Figure 3, in the field of material design, the integration of AI technology has created both new opportunities and challenges for HEA development [31,32,33]. By leveraging its robust data analysis and predictive capabilities, AI can swiftly extract valuable insights from massive datasets, accelerating the discovery and optimization of novel materials [20,34,35]. The technology excels at handling complex multivariate relationships, establishing precise input-output mappings to enable accurate performance prediction and optimization [36,37].

The application of AI technology in material design offers multiple significant advantages [39,40,41]. First, it can significantly accelerate R&D processes by optimizing material combinations through machine learning, thereby shortening development cycles and enhancing efficiency [42,43]. Second, AI technology reduces R&D costs by replacing substantial exploratory experiments with computational methods, enabling the screening of “higher-probability” material structures for validation experiments, thus cutting down on expensive trial-and-error costs [44,45,46]. Additionally, AI technology improves material performance through deep analysis and predictive modeling, assisting in designing materials with superior properties [40,47]. Furthermore, it demonstrates innovative capabilities by extracting key elements from vast data to generate novel material design solutions, driving advancements in materials science [20,39].

In practical applications, AI technology has yielded numerous exemplary cases in material design. For instance, AI algorithms can rapidly screen and analyze vast amounts of material data to identify high-entropy material candidates, significantly boosting screening efficiency [31,48,49]. Material Genome Engineering leverages AI for digital material design by building genomic databases and models, accelerating the development and application of new materials [50,51]. Furthermore, generative AI models like MatterGen can generate material structures based on target properties, offering innovative approaches to material design [34,52,53,54,55]. These examples vividly demonstrate the immense potential and broad prospects of AI technology in material design.

2. AI Technology in HEA Design

2.1. Algorithm Principle and Applicability Analysis

To help readers in the metallurgical field quickly grasp the physical principles and operational essentials of various models, this section uses the traditional “phase diagram calculation —experimental verification” process as an analogy. The algorithm is broken down into three interconnected stages: ① characteristic smelting (input), ② phase region determination (learning), and ③ performance prediction (output).

Table 1. Metallurgical translation of core algorithms.

Category of Algorithm	Metallurgical Analogy	The Role in the Study
Random Forest (RF)	The median is taken after tensile tests with multiple furnace cycles and sampling points	Robust regression or classification baseline
Gradient Boosting (GB)	Continuous refining: each round of remelting for residual error	Single phase/multi-phase classification, F1 highest
Deep neural network(DNN)	High temperature diffusion: inter-layer weights, such as diffusion channels	Mechanical performance end-to-end mapping
Conditions generate adversarial networks(CGAN)	Oriented solidification: Generator = mold, discriminator = quality control	Generate alloy composition on demand
Active learning(AL)	Additional sampling at key experimental points	Pick the alloy with the most information for the experiment under a small sample
Transfer learning(TL)	The strengthening mechanism of low-carbon steel is transferred to high-entropy steel	Accelerate the modeling of new systems using known alloy knowledge

provides a metallurgical interpretation of the core algorithm.

2.1.1. Random Forest and Gradient Boosting—Algorithmic “Multi-Burn-In”

Random forest employs parallel training of numerous decision trees, where each tree utilizes only partial “furnace batches” (sample data) and “features” (attributes), ultimately voting collectively or averaging results. Metallurgically speaking, this approach can be understood as cross-validation using experimental data from different production teams over the years, effectively reducing errors caused by anomalies in individual furnace batches. Gradient boosting, on the other hand, sequentially trains weak learners, continuously optimizing residual errors from previous iterations—much like refining impurities through a process of gradual reduction.

2.1.2. Deep Neural Network—“Diffusion Channel” Perspective

The three-layer fully connected network architecture (input → hidden → output layers) corresponds to the metallurgical process of “composition → micro-structure → properties”. The weight matrix W updates according to an exponential relationship similar to temperature-dependent diffusion coefficients, with gradient descent effectively lowering the system’s free energy. To prevent over-fitting (similar to grain abnormal growth), we implement Dropout = 0.2 after each layer, which acts as a second-phase particle anchoring mechanism.

2.1.3. Conditions Generate Adversarial Networks—“Reverse Design Casting”

Traditional mold casting determines the final shape. The generator G of CGAN outputs components most likely to meet the “target performance” condition vector based on this criterion. The discriminator D distinguishes between the virtual samples generated by G and real experimental data, with both adversarial networks competing until D can no longer differentiate them.

2.1.4. Active Learning—“Sampling Strategy”

Traditional experimental design commonly uses orthogonal tables or uniform designs; active learning replaces manual experience with uncertainty estimates (such as the BALD index) to automatically select alloys with “maximum information” for the next round of smelting. Active learning can reduce hardness prediction errors, which is equivalent to saving experimental costs.

2.1.5. Transfer Learning—“Experience Transfer”

When experimental data are insufficient for new alloy systems (e.g., Nb-Ta-Zr-Hf-Mo), we first pre-train the network on well-documented alloy systems (e.g., Al-Co-Cr-Cu-Fe-Ni). We then freeze weights near the input layer (corresponding to elemental physical properties) while fine-tuning weights near the output layer (corresponding to performance variations). This approach is analogous to transplanting a low-carbon steel dislocation strengthening model to high-entropy steel, requiring only modifications to the dislocation energy-related terms.

2.2. Machine Learning Model

Building on the algorithms, machine learning—ranging from shallow RF to deep CGAN—stands as one of the most prevalent AI technologies in materials design [35]. By developing appropriate models, researchers can effectively predict and optimize the performance of high-entropy materials [56,57]. Active Learning (AL) algorithms, for instance, demonstrate exceptional efficiency in reducing data labeling costs while performing remarkably well on small datasets [58]. Furthermore, methodologies such as generative modeling, data augmentation, and transfer learning have been implemented in high-entropy material design to enhance model performance and generalization capabilities [59,60,61].

Active learning algorithms demonstrate unique advantages in high-entropy material design [62]. By intelligently selecting the most informative data points for labeling within limited resources, they maximize model training efficiency [31,57]. For instance, in designing high-entropy alloys, active learning can precisely identify the most promising alloy compositions from extensive combinations for experimental validation [62]. This approach avoids costly trial-and-error testing of all possible configurations, significantly reducing annotation costs [62]. Moreover, even with small datasets, active learning algorithms continuously improve predictive accuracy and generalization capabilities through iterative optimization, enabling them to perform exceptionally well on small sample sets [58].

Generative models have revolutionized the design of high-entropy materials. These models can generate novel material structures and compositional combinations with potential value by leveraging existing data distributions [63,64]. For instance, in designing high-entropy oxides, generative models can create new oxide materials with similar properties by analyzing the structural characteristics and elemental composition patterns of known stable oxides [31,65]. Not only do these generated materials theoretically exist, but they may also possess unique performance characteristics [31,65]. This approach provides material scientists with abundant research subjects and innovative approaches, significantly expanding the design possibilities for high-entropy materials.

The role of data augmentation techniques in high-entropy material design cannot be underestimated [60]. By reasonably transforming and expanding existing material data through operations such as adding noise, rotating, or flipping the data, we can enhance its diversity and complexity [66,67]. This approach helps improve machine learning models ‘adaptability and robustness to various scenarios, enabling them to perform more reliably across diverse practical applications [68,69,70]. Consequently, it allows for more accurate prediction and optimization of high-entropy materials’ properties [60,71,72,73].

Transfer learning offers cross-domain knowledge transfer advantages for high-entropy material design [57,61]. In materials science, different types of materials often share similarities and commonalities [74]. Transfer learning enables the application of knowledge and model parameters learned from related material fields to high-entropy material design [57,75]. For instance, extracting specific features from solid solution strengthening physical models and transferring them to high-entropy alloy prediction models can reduce reliance on large-scale annotated data in material design [57,59,76]. This approach accelerates model convergence speed, enhances performance and generalization capabilities, thereby allowing rapid breakthroughs in high-entropy material design by leveraging existing research achievements [31,76,77].

2.3. Data Processing and Analysis

AI technology can process and analyze massive amounts of data from experiments and simulations, mining for potential information and patterns [34]. For example, by combining high-throughput computing and experimental screening with machine learning models, the composition and structure of high-entropy materials can be rapidly determined and their properties predicted [78,79,80,81,82].

High-throughput computing plays a pivotal role in high-entropy material design [78,83,84]. Leveraging powerful computational resources, it enables simultaneous theoretical calculations and simulations of large-scale material systems [44,85,86]. For instance, in high-throughput density functional theory (DFT) computations, researchers can predict multiple aspects, including electronic structures, mechanical properties, and thermodynamic characteristics of high-entropy compounds with diverse compositions and architectures [87,88,89]. This approach allows rapid screening of potentially valuable material combinations within short timelines, significantly accelerating material discovery [87,90]. Moreover, the massive datasets generated through high-throughput computing serve as training foundations for machine learning models, providing rich and accurate data support that enhances model reliability and predictive capabilities [87,91].

Experimental screening is an indispensable component in high-entropy material design [81,92]. By integrating high-throughput experimental techniques, researchers can rapidly synthesize and characterize large-scale material samples in the laboratory [93,94,95]. For instance, high-throughput melting technology enables the simultaneous preparation of multiple high-entropy alloy specimens with varying compositions, followed by performance evaluations through rapid testing methods such as hardness measurement and tensile testing [84,96]. Integrating these experimental data with machine learning models allows for further optimization of model parameters and architectures, thereby enhancing the accuracy of material property predictions [76,97]. This approach enables the precise determination of high-entropy materials’ composition and structure, providing reliable candidate materials for practical applications [79,83,87].

AI technology, when processing and analyzing high-entropy material data, can uncover hidden information and patterns beneath the surface [31,98]. Through multivariate statistical methods like cluster analysis and principal component analysis applied to massive experimental and computational datasets, researchers can reveal intrinsic connections between material compositions, structures, and properties [99,100]. For instance, clustering analysis groups high-entropy materials with similar performance into clusters, thereby identifying potential correlations between specific elemental combinations or structural features and material characteristics [101,102]. These insights are crucial for guiding further design and optimization of high-entropy materials, enabling materials scientists to gain a deeper understanding of material fundamentals and develop targeted high-entropy materials more efficiently.

2.4. Performance Prediction

An AI model can establish the complex nonlinear relationship between the structure and properties of materials and realize the accurate prediction of the mechanical and physical properties of HEAs [40,103]. This helps to quickly screen out HEAs with potential application value, reducing the cost and time of experiments [104].

AI models demonstrate unique advantages in establishing complex nonlinear relationships between the structure and properties of HEAs [31,57]. Traditional material performance prediction methods, often based on simple linear models or empirical formulas, struggle to accurately describe the characteristics of multi-component systems like HEAs [105]. However, as shown in Figure 4, AI models such as neural networks can automatically learn complex mapping relationships between inputs (material composition, structure, etc.) and outputs (performance metrics) from massive datasets, even when these relationships are nonlinear and highly coupled [31,57]. For instance, when predicting yield strength in high-entropy alloys, AI models can comprehensively consider factors like elemental content, crystal structure, and grain size that influence strength. They also capture interactions and synergistic effects among these elements, thereby establishing accurate predictive models [106].

Accurate performance prediction plays a vital role in the rapid screening of high-entropy materials [107]. In practical applications, high-entropy materials often need to meet specific performance requirements. Through AI model predictions, researchers can efficiently identify the most promising candidates among numerous material options that best satisfy target performance criteria [108,109]. For instance, in aerospace engineering, there is a critical demand for high-entropy alloys that combine high strength, low density, and high-temperature resistance [110,111]. By leveraging AI models to predict properties of various alloy compositions and structures, scientists can quickly pinpoint several alloy systems with potential applications. Subsequent experimental verification and optimization of these systems significantly reduce the number and scope of required experiments, thereby substantially cutting experimental costs and time consumption [104].

The application of AI technology in predicting the performance of HEAs not only accelerates material development but also provides robust support for optimized design [112,113]. By analyzing and providing feedback on prediction results, it guides adjustments to material composition and structure, further enhancing performance [114,115]. For instance, if predictions indicate that a particular HEA has relatively low electrical conductivity with potential improvement, targeted adjustments can be made based on sensitivity analysis results from the model [3,89]. Subsequent re-prediction through the model enables gradual optimization of material properties until satisfactory outcomes are achieved.

2.5. Limitations of DFT and MD in HEA Modeling

Although density functional theory (DFT) and classical/machine learning molecular dynamics (MD) have become the core means of high entropy alloy (HEA) high-throughput screening and phase stability evaluation, their computational feasibility and prediction accuracy in multi-component systems face new challenges and need to be critically examined.

2.5.1. Calculate the Expansion Law of Cost with the Number of Master Elements

DFT: For a supercell containing M principal elements and N atoms, the number of electronic step self-consistent iterations increases linearly with M. To maintain equivalent accuracy, the k-point grid requires densification, resulting in a total CPU time ∝$\mathrm{M}·{\mathrm{N}}^3·\mathrm{N}{\mathrm{k}}^3$. Taking a 500-atom Co-Cr-Fe-Mn-Ni model as an example, single-point energy calculations already require approximately 10⁵ CPU-h. If the principal elements are increased to 7, the computational cost would at least triple or quadruple.

MD: The empirical potential parameter increases with the binary combination number $\mathrm{M}(\mathrm{M}-1)/2$. The machine learning potential training set must cover all $\mathrm{M}(\mathrm{M}-1)(\mathrm{M}-2)/6$ ternary sub-spaces, with data generation costs ∝ M³. A single run of the million-atom GPU-MD still requires 10⁴–10⁵ GPU-hours.

Limitations of special quasi-random structure (SQS): In order to ensure randomness, the super-cell needs to be magnified by $\sqrt{\mathrm{M}}$ times, resulting in the growth of N with M², further exacerbating the exponential expansion.

2.5.2. The Accuracy of Phase Stability Prediction

Exchange correlation functions and magnetic entropy: Magnetic dipoles and phonon entropy exhibit reversible relative stability at high temperatures, yet are neglected in 0 K DFT calculations.

Short-range order (SRO) deficiency: When SRO length exceeds 6 Å, SQS fails to capture genuine chemical correlations, resulting in mixed enthalpy errors of 10–20 meV/atom and phase boundary temperature deviations of 100–200 K.

MD time scale: Experimental annealing requires 10²–10⁴ s, while classical MD can only simulate 10⁻⁷ s, failing to capture slow amplitude decomposition or σ phase separation. Potential function migration errors cause dislocation energy deviations up to 40%, directly affecting the driving force of the FCC-HCP phase transition.

In summary, DFT and MD face dual “precision-cost” bottlenecks in high-entropy alloys (HEA) scenarios. Future efforts should integrate CALPHAD, machine learning-based potential active learning, and experimental feedback to construct a multi-scale hybrid framework that balances accuracy and efficiency.

3. Application Cases of AI in HEA Design

3.1. Component Design

3.1.1. Application of the Generation Model in Refractory HEA Design

A research team from Pennsylvania State University published in the Journal of Materials Informatics demonstrates that generative models represent a highly promising new approach for HEA design [116]. By employing Conditional Generative Adversarial Networks (CGAN) with additional conditional vectors to control outputs, they successfully established a mapping relationship between latent space and desired performance metrics [116], as shown in Figure 5. The generator learns probability distributions of alloy compositions and properties to generate samples meeting specific requirements [116], as shown in Figure 6. The study pioneers a novel pathway for high-entropy alloy composition design, showcasing AI’s immense potential in material innovation [116].

3.1.2. Design of Multi-Objective Optimization Framework for Refractory HEAs

The research team led by Shu Yanjing at Beijing University of Science and Technology has developed a multi-objective optimization (MOO) framework that integrates machine learning, genetic search, cluster analysis, and experimental feedback to design refractory high-entropy alloys (RHEAs) with superior high-temperature strength and room-temperature ductility [117]. The team synthesized 24 distinct RHEAs and experimentally validated the exceptional performance of the Zr-Nb-Mo-Hf-Ta alloy under high-temperature conditions [117]. This study not only demonstrates AI’s application in material design but also validates the reliability of AI models through experimental verification, providing crucial references for RHEA development [117].

3.1.3. Comparison and Discussion

While generative models (e.g., CGAN) and multi-objective optimization frameworks (e.g., MOO) demonstrate significant potential in component design, they differ markedly in methodology and application scenarios. The Penn State team’s CGAN model focuses on implicit mapping between components and performance metrics, making it suitable for reverse engineering in high-dimensional spaces—particularly effective when lacking explicit physical models. In contrast, the MOO framework developed by the University of Science and Technology Beijing emphasizes balancing multiple objectives with experimental feedback, exhibiting strong engineering-oriented characteristics. Notably, both approaches rely on high-quality data inputs, and the “black box” nature of generative models still limits their application in mechanistic interpretation. Future research could explore integrating generative models with MOO to establish a closed-loop design process that transitions from “generating candidates” to “multi-objective optimization.”

3.2. Phase Structure Prediction

3.2.1. Classification and Prediction of HEA Phase Composition by Deep Learning Algorithm

The team from the University of Science and Technology Beijing (USTB) has developed a novel strategy, as shown in Figure 7, which uses genetic algorithms (GA) to automatically generate element numerical descriptors for high-entropy alloys (HEAs). This breakthrough method overcomes limitations of traditional empirical features, significantly improving prediction accuracy in phase structure analysis [118]. As shown in Figure 8, the system achieved 90.2%, 88.1%, and 82.7% accuracy rates in face-centered cubic (FCC), body-centered cubic (BCC), and bimodal classification tasks, respectively [118]. By dynamically optimizing the element descriptor space, they established a closed-loop system integrating “feature generation, model training, and experimental feedback,” providing a universal framework for data-driven material design [118].

3.2.2. Combination of Conditional Generation Adversarial Network and Active Learning

A machine learning model combining Conditional Generative Adversarial Networks (CGAN) and Active Learning (AL) has been developed to predict the body-centered cubic BCC phase, face-centered cubic FCC phase, and BCC + FCC phase [119] in high-entropy alloys. The model first employs domain knowledge embedding for feature selection, then utilizes CGAN to expand the data-set from 1016 samples to 1616. Machine learning training is conducted using the augmented data, followed by active learning techniques to enhance prediction accuracy. The final model achieved a precision rate of 96.08% [119].

3.2.3. Element Feature Transfer Adversarial Network

As shown in Figure 9, a research team from Tsinghua University has developed an algorithm framework combining Information Maximization Generative Adversarial Networks (InfoGAN) and Elementar Convolutional Graph Neural Networks (ECGNN)—known as the Elementar Feature Transfer Adversarial Network (EFTGAN)—for predicting properties of high-entropy alloys [61]. This network extracts elemental features from crystal atomic and structural information while generating new feature representations for prediction targets. By avoiding computationally intensive structural calculations and employing iterative methods, it significantly enhances prediction accuracy [61], as shown in Figure 10.

3.2.4. Comparison and Discussion

In phase structure prediction, genetic algorithms demonstrate high accuracy in classification tasks through element descriptor generation, particularly showing innovation in addressing the limitations of traditional feature representation. The CGAN-active learning strategy significantly improves small-sample prediction performance through data augmentation and active sampling. EFTGAN further integrates graph neural networks with feature transfer mechanisms, maintaining high precision while reducing reliance on first-principles calculations. These three approaches advance phase prediction accuracy and efficiency from three distinct perspectives: feature engineering, data augmentation, and feature learning. However, each faces challenges such as high model complexity, poor interpretability, and excessive computational demands. Future research could explore hybrid models combining multiple advantages to balance precision, efficiency, and explainability.

3.2.5. The Gap Between Machine Learning Predictions and Real Synthetic Dynamics

Although existing artificial intelligence prediction models demonstrate high accuracy (≥90%, see Section 3.2.1, Section 3.2.2 and Section 3.2.3) in distinguishing FCC, BCC, and duplex structures, most of these criteria are based on equilibrium or quasi-equilibrium thermodynamic assumptions, failing to adequately reflect the transient dynamic behaviors observed in actual powder metallurgy or additive manufacturing processes. Sharma et al. [12], in their study on intermetallic crystallization in magnesium-based high-entropy alloy powders, employed a two-step process combining high-energy ball milling and discharge plasma sintering (SPS) and observed the following phenomena: Continued ball milling beyond 5 h resulted in sharp peaks being fully incorporated into a broadened “mantle peak”, indicating that the intermetallic phase had undergone type-III crystallization via dislocation-induced and nanocrystalline boundary pathways. TEM dark-field images further revealed grain sizes < 5 nm, confirming that high-density defects drove the phase transformation. Notably, even after annealing at 500 °C/6 h, no recrystallization signs were observed in the amorphous phase (XRD mantel peaks remained essentially unchanged), demonstrating that under the combined effects of high cooling rates (~10⁶ K s⁻¹) and high defect density, the system had fallen into a deep potential well far beyond the FCC/BCC/HCP stable regions predicted by equilibrium phase diagrams. This result directly indicates that AI models failing to incorporate the “cooling rate-amorphous formation capability” dynamic parameter into their feature space will significantly deviate from experimental observations in predicting phase stability. Therefore, future models must integrate three critical components: (1) transient phase transition pathways obtained through in-situ synchrotron radiation XRD; (2) process descriptors characterizing cooling rates and defect densities (including local shear rate, dislocation density, and lattice mismatch degree); (3) non-equilibrium thermodynamic/dynamic databases such as Kinetic—CALPHAD. Crucially, incorporating formation energy of dislocations, excess energy at nanocrystalline boundaries, and multi-component lattice distortion barriers is essential for reliably predicting phase stability in non-equilibrium processes like additive manufacturing or high-energy ball milling.

3.3. Performance Optimization

3.3.1. Synergistic Optimization of High Temperature Strength and Room Temperature Toughness of HEAs

A research team from Beijing University of Science and Technology developed a multi-objective optimization (MOO) framework integrating machine learning, genetic search, cluster analysis, and experimental feedback. This framework was designed to identify the optimal alloy composition [117] for refractory high-entropy alloys (RHEAs) that achieve both high-temperature strength and room-temperature ductility. The study concluded that the Zr-Nb-Mo-Hf-Ta alloy system demonstrated exceptional high-temperature application potential. Specifically, the Zr_0.13Nb_0.27Mo_0.26Hf_0.13Ta_0.21 alloy exhibited a yield strength approaching 940 MPa at 1200 °C and a room-temperature fracture strain of 17.2% [117]. Its remarkable heat resistance and excellent structural stability indicate significant potential for structural applications in high-temperature environments [117].

3.3.2. Hardness Optimization of Al-Co-Cr-Cu-Fe-Ni HEAs

Using machine learning models, researchers screened and prepared 42 high-entropy alloys [117] from a compositional space containing nearly 2 million alloy types. Among these, 35 alloys demonstrated hardness exceeding the highest values in the training samples, achieving 83.3% performance optimization. Notably, 17 alloys saw hardness improvements surpassing 10%, with the most significant enhancement reaching 14% [117].

3.3.3. Comparison and Discussion

In terms of performance optimization, the MOO framework achieved synergistic optimization of high-temperature strength and room-temperature toughness in refractory high-entropy alloys, demonstrating the value of multi-objective optimization in balancing complex performance parameters. Meanwhile, hardness optimization in the Al-Co-Cr-Cu-Fe-Ni system showcased machine learning’s efficiency advantages in high-throughput screening. These examples highlight the importance of integrating multi-objective search algorithms with experimental validation to ensure practical applicability. Notably, the former relies more on experimental feedback and domain knowledge guidance, while the latter emphasizes data-driven exploration of compositional space. Both cases indicate that performance optimization must be closely integrated with practical application scenarios and experimental verification, as purely data-driven approaches or theoretical predictions cannot fully replace experimental validation. In addition, the current approaches are often limited to specific alloy systems and properties. Future research should expand to more complex performance targets, such as fatigue resistance, corrosion behavior, and thermal stability, and incorporate multi-scale modeling to bridge micro-structural features with macroscopic properties.

3.4. Material Screening and Discovery

3.4.1. Cu-Ni-Co-Si HEA System

In a study, Pan et al. [120] developed a data set for the Cu-Ni-Co-Si high-entropy alloy system, evaluating hardness and electrical conductivity as performance metrics. They trained three models: ordinary least squares (OLS), artificial neural networks (ANN), and random forest (RF) [120]. Comparative analysis demonstrated that the RF model exhibited superior predictive accuracy, making it the preferred tool for final predictions [120], as shown in Figure 11. Through predictive analysis of 38,880 potential alloy compositions and process combinations using this RF model, four optimal combinations were identified [120]. Experimental validation successfully produced an alloy composition with low cobalt content (Cu-2.3Ni-0.7Co-0.7Si) while maintaining excellent overall performance [120].

3.4.2. Low Thermal Expansion Coefficient HEA

Rao [121] and his collaborators developed an active learning-based strategy for efficiently screening high-entropy alloys with low thermal expansion coefficients (TEC) [121]. The approach first constructs a potential alloy space using a generative model (GM), then samples 1000 candidate compositions likely to exhibit low TEC [121] through Markov Chain Monte Carlo (MCMC) methods. After further screening the top 10–30 candidates, density functional theory (DFT) and thermodynamic calculations are employed to obtain supplementary input such as magnetization characteristics [121]. An ensemble model evaluates these candidate alloys, selecting the top three for experimental synthesis and testing. If experimental results fail to meet requirements, the new data is fed back into the initial dataset to initiate iterative optimization [121]. Through six rounds of iterations, 18 alloys were synthesized (17 novel components and 1 known component), ultimately identifying two high-entropy alloys demonstrating ultra-low thermal expansion coefficients (approximately $2\times {10}^{-6}{\mathrm{K}}^{-1}$) at 300 K [121].

3.4.3. Nb-Ta-Zr-Hf-Mo Refractory HEA System

To explore the optimal balance between high-temperature strength and room-temperature ductility in the Nb-Ta-Zr-Hf-Mo refractory high-entropy alloy system, Wen et al. [122] proposed a machine learning strategy integrating prediction uncertainty analysis with clustering algorithms [122]. This approach employs Expected Improvement (EI) values as the key metric, which simultaneously considers both predicted performance metrics (high-temperature strength and room-temperature ductility) and their uncertainties [122]. By ranking the two EI values for each alloy in the search space, candidates on the Pareto frontier were identified [122]. Subsequent cluster analysis guided the selection of optimal solutions [122]. Ultimately, this strategy successfully led researchers to discover and synthesize four novel alloys that exhibit excellent high-temperature strength and good room-temperature ductility [122].

3.4.4. Single-Phase Refractory HEA

Yan et al. [123] researchers focused on discovering novel single-phase refractory high-entropy alloys. They constructed a dataset containing eight key characteristics and 1807 records, training nine different classification models for prediction [123]. By comparing evaluation metrics such as the F1 score, the Gradient Boost (GB) model demonstrated optimal performance with a classification accuracy rate of 97.37% [123]. Using this GB model, the researchers successfully predicted over 100 potential single-phase, oxidation-resistant refractory high-entropy alloy compositions [123]. Subsequently, they selected and synthesized 10 alloys for experimental verification [123]. X-ray diffraction (XRD) analysis confirmed that all synthesized alloys exhibited single-phase structures, strongly validating the reliability of machine learning predictions [123].

3.4.5. Comparison and Discussion

In material screening and discovery, research teams have employed diverse strategies, including random forest (RF), active learning (AL), ensemble models, and cluster analysis. These approaches have significantly accelerated the identification of Heads-Up Alloys (HEAs) with desirable properties. Pan et al. successfully predicted hardness and electrical conductivity in Cu-Ni-Co-Si systems using RF models, demonstrating the stability of tree-based models in regression tasks. Rao et al. achieved efficient discovery of alloys with low thermal expansion coefficients through a combination of generative models and active learning. Wen and Yan’s team identified novel high-performance materials in refractory high-entropy alloys using uncertainty-guided clustering analysis and gradient boosting classification models, respectively.

However, the reliance on pre-defined feature sets and the limited diversity of training data remain major constraints. Moreover, the transition from prediction to experimental synthesis is not always straightforward, often requiring iterative refinement. Future efforts should prioritize the development of unified, open-source platforms that integrate prediction, synthesis, and characterization into a closed-loop system, enabling more efficient and reproducible material discovery.

3.4.6. Limitations and Mitigation Strategies of Combinatorial Synthesis

While combinatorial synthesis has significantly accelerated the screening efficiency of Heterogeneous Eutectic Alloys (HEAs), its effectiveness remains constrained by three major challenges: compositional gradients, phase separation, and equilibrium conditions. Taking magnetron sputtering of thin films as an example, significant compositional gradients at the 1 μm scale can lead to localized enriched phases that deviate from the single-phase structures predicted by machine learning models. Additionally, laser-melted RHEAs retain metastable FCC phases due to excessively high cooling rates (~10⁴ K/s), though their volume fraction can be optimized through coupled phase-field simulations and active learning algorithms to refine annealing processes. These findings indicate that future combinatorial synthesis must be deeply integrated with real-time characterization techniques (such as synchrotron radiation XRD) and dynamic process simulations to achieve closed-loop optimization through the “design-synthesis-verification” cycle.

4. Challenges of AI Technology in HEA Design

Although AI technology has made significant progress in the design of HEAs, there are still some challenges.

4.1. Data Related Issues

4.1.1. Scarcity of High-Quality Data

High-quality data is crucial for training and optimizing AI models, yet experimental data on HEAs remains relatively scarce [31,57]. Due to their complex multi-component nature, HEAs present significant challenges in experimental preparation and characterization, leading to higher costs [32,124]. This inherently results in less comprehensive data compared to traditional materials. Moreover, the limited data available still requires improvement in quality and consistency. The varying experimental methods, testing conditions, and data recording approaches used by different research teams further undermine comparability and consistency [125,126]. For instance, some studies employ different strain rates and test temperatures when evaluating the mechanical properties of HEAs, making direct data comparison and integration difficult. This scarcity of high-quality data restricts both the quantity and diversity of training samples for AI models, ultimately affecting their performance and generalization capabilities [127].

4.1.2. Data Skew and Lack of Representativeness

Current HEA data often remain concentrated within specific compositional systems or performance ranges, leading to significant data bias [73,128,129]. For instance, extensively studied HEAs predominantly focus on common metal-element combinations, while data on alloys containing rare or special elements remain relatively scarce [73,128]. This imbalance may cause AI models to over-fit specific material types during training while under-performing in predicting other underrepresented systems. Furthermore, insufficient data representativeness increases the risk of model errors when encountering unknown scenarios in practical applications, ultimately diminishing both the reliability and practical utility of these models [49].

4.1.3. Negative-Sample Deficit

In the current published data set of high entropy alloys (HEA), most entries are alloys that have been successfully synthesized by experiments, while the “unsynthetic” examples (negative samples) that have been explicitly disproven by experiments only account for a minority. This imbalance exposes models to insufficient negative examples during training, causing systematic deviations in decision boundaries regarding synthesis feasibility—potentially underestimating the actual feasible region. Without adequate negative sample guidance, models struggle to accurately characterize the critical features distinguishing “synthesizable from unsynthetic” alloys, leading to unreliable predictions when handling compositional fine-tuning or extreme process conditions. Such boundary cognitive bias not only escalates experimental verification costs but also delays the discovery of novel alloy systems, ultimately diminishing the practical value of AI-assisted material design in real-world R&D processes.

4.2. Insufficient Model Interpretation

The lack of interpretability in AI models poses another critical challenge, hindering deep understanding of the physical-chemical mechanisms underlying materials [40,130]. While advanced AI models like deep neural networks excel at predicting HEA properties, their internal decision-making processes and predictive foundations often resemble a “black box”—difficult to explain clearly [131,132]. In HEA design, understanding these physical-chemical mechanisms is crucial for optimizing performance and developing novel materials. For instance, when an AI model predicts excellent mechanical properties in a HEA, researchers seek to identify specific microscopic structural features or inter-atomic interactions that contribute to this performance. However, due to insufficient model interpretability, key information cannot be directly extracted from the models [131,132]. This not only limits researchers’ mastery of traditional material science knowledge but also further restricts its application in material design.

4.3. Cross-Domain Transferability

Current machine learning potential models or performance models are often confined to single crystal lattice types and narrow compositional-valence electron space ranges, resulting in significant cross-system migration gaps. For instance, while extensive research focuses on FCC alloys, training datasets for BCC systems and alloys with varying valence electron concentrations remain relatively scarce. This imbalance causes models to overfit specific crystal lattices and electronic structures during training. Consequently, when applied to target systems with significant differences in crystal types or valence electron concentrations, the model’s reliability plummets, thereby diminishing its practical value in unexplored compositional spaces.

4.4. Extrapolation Risk When Far from the Training Distribution

Current machine learning potential functions (ML-PFs) demonstrate significant limitations in extrapolation performance when operating beyond their original training distribution regions, creating substantial computational bottlenecks. Specifically, most training datasets only cover limited temperature-pressure windows and typical crystal defect configurations. This results in dramatic amplification of energy and force prediction errors when models encounter unexplored configurational spaces such as extremely high pressures, metastable phases, or non-equilibrium defect structures. In these out-of-distribution scenarios, ML-PFs may generate physically inconsistent outputs like negative binding energies or erroneous potential surfaces, leading to dynamic simulation failures or distorted thermodynamic calculations. As the extrapolation distance increases, errors accumulate nonlinearly, ultimately compromising the reliability and practical applicability of ML-PFs in high-value applications such as material design and extreme condition predictions.

4.5. Interdisciplinary Integration Issues

The integration of AI technology with traditional materials science knowledge remains insufficient, necessitating enhanced interdisciplinary collaboration and communication [133,134]. Currently, the application of AI in HEA design is primarily driven by researchers in materials science, while professionals from computer science, mathematics, and related fields participate relatively infrequently [133,134]. This shortage of cross-disciplinary talent often results in researchers struggling to fully leverage cutting-edge AI technologies and advanced methodologies. For instance, some materials scientists may lack sufficient understanding of complex machine learning algorithms and data processing techniques, leading to difficulties in building and optimizing AI models. Conversely, computer scientists might not have deep expertise in HEA’s specialized requirements and practical needs, making it challenging to design AI solutions that fully meet material design demands. Furthermore, differences in terminology, research methodologies, and cognitive frameworks across disciplines increase communication barriers and collaboration costs [135,136], undermining the efficiency and quality of interdisciplinary cooperation. These factors collectively constrain the development and application of AI technology in HEA design.

4.6. Typical Case: “Predictive-Synthetic” Bias

Sharma et al. [12] found in the study of Mg-based high entropy alloys that when the cooling rate reached 10⁴ K/s, the alloy predicted by machine learning as “stable BCC phase” actually underwent amorphization rather than forming a solid solution, resulting in the failure of prediction.

4.6.1. Problem Causes

The reasons for the bias are (1) Incomplete dynamic parameters: Current AI models solely rely on thermodynamic descriptors (e.g., ΔHmix and Ω parameters), neglecting cooling rate (CR) and amorphous formation capability (GFA). (2) Insufficient negative samples: The training set is mostly “synthetic” data, and there is a lack of “failure to synthesize” cases (such as amorphization or phase separation), which leads the model to overestimate the stability boundary.

4.6.2. Solutions

The solution is as follows:

(1)

Introduction of Dynamic Descriptors

New Feature: Incorporate cooling rates (CR, unit K/s) and GFA parameters (e.g., $\Delta \mathrm{T}\mathrm{ₓ}={\mathrm{T}}_{\mathrm{l}}-\mathrm{T}\mathrm{ₓ}$, where T_l is the liquidus temperature and Tₓ is the amorphization critical temperature) into the model input.

Implementation Method: Obtain transient phase transition pathways under different CRs through synchrotron radiation in-situ XRD, establishing a CR-ΔTₓ-phase structure correlation database.

(2)

Negative Sample Augmentation and Active Learning Closed-loop

Based on 500 “synthesis failure” records generated by Kinetic-CALPHAD, supplementary data were added, and high uncertainty samples were prioritized for experimental verification through active learning (BALD index). Process is as follows:

Step 1: Retrained the Gradient Boosting (GB) model with an augmented dataset,

Step 2: During experimental verification, if the synthesis results deviate from the prediction (such as amorphization), they are fed back to the negative sample library and iterated for three rounds to reduce the error.

(3)

Cross-scale verification—Digital twin

The influence of cooling rate on FCC/BCC ratio is predicted by phase field simulation, and the comparison with laser melting experimental data verifies the reliability of the model in the process parameter space.

5. Future Development Direction

In the future, the application of AI in the design of HEAs will move from “tool assistance” to “intelligent co-creation”, and its development will be systematically upgraded around four dimensions: algorithm, data, experimental closed-loop and industrialization.

At the algorithmic level, there is a pressing need to develop specialized models for HEA complex feature spaces. Current mainstream approaches predominantly adopt frameworks from image processing and natural language processing. Future efforts should focus on constructing “physics-guided neural networks” that integrate physical constraints, embedding thermodynamic stability, electronic structure characteristics, and diffusion kinetics into loss functions or network architectures to enhance model extrapolation capabilities and physical interpretability. Simultaneously, developing meta-learning and Bayesian optimization frameworks for small-sample scenarios is crucial. By leveraging prior knowledge transfer and uncertainty quantification, these frameworks can mitigate over-fitting risks caused by data scarcity, provide confidence intervals for predictions, and offer risk metrics for experimental decision-making. Furthermore, generative AI will evolve toward multi-modal and multi-objective systems. Through integration of diffusion models and reinforcement learning, this approach enables full-chain reverse design spanning “components—process—micro-structure—performance,” allowing users to input natural language requirements and receive experimentally verifiable candidate material solutions.

Secondly, at the data level, it is essential to establish a high-quality HEAs database covering the “computational-experimental-literature” trinity. On one hand, we should promote standardization of high-throughput computing by creating unified computational protocols and error evaluation systems to ensure the reliability and comparability of simulation data from DFT and molecular dynamics. On the other hand, intelligent experimental data platforms must be developed using IOT sensors, automated lab robots, and blockchain certification technology to enable real-time collection, cleaning, and sharing of experimental data. Simultaneously, large language models (LLMs) should be utilized to extract latent information from massive literature databases, addressing gaps in the “gray literature” and “negative samples” while establishing a dual-loop system integrating literature and databases. Ultimately, this will create a comprehensive knowledge graph for HEAs spanning composition, processing techniques, micro-structures, and service performance, supporting continuous evolution through active learning and generative models.

Thirdly, at the experimental closed-loop level, it is essential to establish a rapid iteration system of “AI prediction-experiment verification-model iteration”. Future laboratories will evolve into AI-experiment collaborative “autonomous material discovery platforms,” achieving full-process automation of material synthesis, testing, and characterization through robotic experimental platforms and online characterization technologies (such as in-situ radiation characterization and atomic probe tomography). AI models will receive real-time experimental feedback, executing the “experiment-characterization-feedback” closed loop 24/7. By leveraging online learning and proactive learning strategies, these models dynamically adjust experimental protocols to maximize information gain per experiment. Simultaneously, digital twin technology should be developed to create virtual experimental environments. This virtual-real interaction approach reduces trial-and-error costs and R&D cycles while enabling predictions of material behavior under extreme conditions (such as ultra-high temperatures and high pressures).

Finally, at the industrial implementation stage, it is crucial to promote large-scale application of AI-designed HEAs in key sectors such as energy, aerospace, and electronics. On one hand, we should develop low-code/no-code AI design platforms tailored for engineers, encapsulating expert knowledge and model templates to enable material engineers to perform composition optimization and process window exploration through drag-and-drop operations. On the other hand, establishing a “materials-process-equipment” collaborative optimization platform is essential. This platform should deeply integrate AI design outcomes with additive manufacturing and powder metallurgy process parameters, effectively addressing performance degradation issues during the transition from laboratory achievements to engineering applications. Additionally, collaborating with equipment manufacturers to establish interface standards for AI design, additive manufacturing/powder metallurgy/spray coating processes will bridge the “last mile” gap between virtual designs and physical components.

Taking Figure 12 as an example, it demonstrates a complete high-throughput material screening workflow comprising five key components: rapid synthesis, sample preparation, mechanical testing, rapid characterization, and the integration of big data with AI/ML models. However, this process still faces three often-overlooked bottlenecks in the HEA field: (1) Data quality and consistency issues. The “rapid characterization” phase in Figure 12 relies on automated equipment, but discrepancies in testing standards across laboratories (e.g., strain rate, temperature control precision) can lead to data bias. Such deviations significantly reduce the generalization capability of AI models, necessitating the establishment of cross-platform calibration protocols. (2) Risk of failure in the active learning closed-loop. While the cycle between “AI/ML models” and “experimental verification” appears efficient in Figure 12, practical applications may fall into local optima due to “negative sample deficiency,” resulting in overly optimistic model predictions. We recommend incorporating a “failed synthesis case recovery mechanism” to incorporate unsuccessful synthesized components into subsequent training rounds. (3) Breakdown in process-performance mapping. The workflow fails to explicitly demonstrate how process parameters (e.g., cooling rates) influence final phase structures, potentially leading to deviations from predicted outcomes. Future improvements should introduce real-time in-situ characterization (such as synchronous radiation XRD) during the “rapid synthesis” phase to input process parameters as dynamic features into the model. In summary, Figure 12’s high-throughput paradigm requires three enhancements to unlock its full potential: ① Establishing cross-platform data calibration standards; ② Enforcing negative sample feedback; ③ Integrating process parameters with real-time characterization. These changes will upgrade the AI experiment loop from an “efficiency tool” to a “reliable discovery engine”.

6. Conclusions

In summary, artificial intelligence is reshaping the design paradigm of HEAs with unprecedented depth and breadth. From iterative theoretical models to explosive growth in experimental data, and through cross-scale and interdisciplinary collaborative innovation, AI has evolved from an “optional tool” to a “core infrastructure”. This paper systematically reviews the latest advancements in AI applications for HEA composition design, phase structure prediction, performance optimization, and new material discovery. Through a series of case studies, it validates the feasibility and superiority of AI methodologies, driving a paradigm shift from “experience-driven” to “data-driven” material design. However, it must be clearly recognized that the current stage remains merely the prelude to “AI + HEA” integrated innovation, with significant gaps remaining before achieving truly intelligent material engineering.

First, data remains the primary productive force. While generative models and transfer learning demonstrate remarkable performance in small-sample scenarios, the multi-component and multi-scale nature of HEAs inherently leads to exponentially expanding performance landscapes. No model can sustain long-term development without continuous feeding from high-quality, large-scale datasets. Moreover, the high cost and heterogeneity of experimental data exacerbate the “small samples, low quality, strong bias” issue, which becomes a critical constraint on model generalization capabilities. In addition, it must be emphasized that AI is still mainly applicable to “interpolative search in the known element space” rather than “global extrapolative invention”; the fundamental limitations that have not been solved are the missing negative samples, cross-system drift and the blind area of extrapolation away from the training distribution.

Secondly, the black box dilemma. The unexplainability of deep models hinders the deep integration of “data-physical” knowledge, making it difficult for researchers to extract scientific laws and physical mechanisms that can guide experimental design from the prediction results, and makes key fields (such as nuclear use and medical use) doubt the reliability of AI-designed materials.

Finally, disciplinary barriers pose significant constraints. The design of HEAs requires interdisciplinary expertise spanning physics, chemistry, metallurgy, computer science, and automation. Teams with single-discipline backgrounds struggle to manage the entire process. The “language barrier” between materials science and AI technologies has prevented advanced algorithms like graph neural networks and reinforcement learning from fully unlocking their potential in material research. Moreover, the shortage of cross-disciplinary talent creates critical gaps in the innovation chain.

To advance future development, AI-driven design of HEAs requires establishing a four-dimensional collaborative paradigm integrating “data-algorithm-experiment-industry”. At the algorithmic level, specialized models for HEAs must be developed, including physics-guided neural networks with physical constraints, meta-learning frameworks, and Bayesian optimization systems. Generative AI should be employed to enable full-chain reverse engineering. The data dimension demands building a high-quality database of HEAs, standardizing high-throughput computing, developing intelligent experimental platforms, and leveraging large language models to extract the literature data for constructing a “knowledge graph” of HEAs. For closed-loop experimentation, an AI-experiment collaborative platform should automate the entire process while implementing digital twin technology to reduce trial-and-error costs and support extreme condition predictions. In industrial implementation, key applications of AI-designed materials should be promoted through low-code platforms and collaborative optimization mechanisms linking “materials-process-equipment”, bridging the gap between virtual designs and physical components.

In summary, the integration of AI with HEAs represents not merely a technological breakthrough but a profound paradigm shift in scientific research. This transformation requires collaborative efforts from materials scientists, computer engineers, industry stakeholders, and policymakers. Through open collaboration, we can establish an innovative ecosystem of “AI + materials” that ultimately enables the vision of designing materials with precision, efficiency, and on-demand adaptability.