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High-entropy alloys (HEAs) represent a paradigm shift in materials design and exhibit exceptional mechanical properties due to their multi-principal-element compositions. However, the vast compositional space poses significant challenges for traditional design approaches, which require innovative theoretical frameworks to guide the discovery of alloys with specific attributes, such as enhanced strength, ductility, and toughness. This conceptual study proposes a novel framework leveraging deep generative models to systematically explore and generate HEA compositions tailored to targeted mechanical properties. Drawing on principles from machine learning and materials physics, the framework integrates latent-space representations of alloy features, including valence-electron concentration and mixing enthalpy, to enable the conditional generation of virtual alloys. By synthesizing recent literature on HEAs and generative modeling in materials science, we establish the theoretical foundations of this approach and emphasize its potential to accelerate rational design without empirical validation. The proposed model addresses key limitations in current methodologies by incorporating uncertainty quantification and multi-objective optimization in a purely conceptual manner. This research advances the theoretical discourse in applied artificial intelligence for materials science, providing a blueprint for future conceptual explorations in alloy engineering. Ultimately, the framework envisions a transformative role for deep generative models in navigating the complexity of HEA design spaces.

Phase transformations in multi-component alloys underpin microstructural evolution and performance in advanced engineering systems. Yet, their prediction remains a persistent challenge due to the interplay of thermodynamic complexity, kinetic constraints, and sparse data regimes. While machine-learning approaches have shown promise in accelerating materials discovery, purely data-driven models often lack physical fidelity, interpretability, and robustness when extrapolated across high-dimensional compositional spaces. This paper introduces a conceptual framework for physics-constrained neural networks (PCNNs) to predict phase transformation pathways rather than static equilibrium states in multi-component alloy systems. The framework embeds thermodynamic and kinetic principles directly into the learning objective, reframing physical laws as epistemic constraints that govern admissible predictions. Unlike conventional physics-informed neural networks that solve predefined equations, the proposed approach integrates higher-level physical criteria—such as Gibbs free-energy minimization, phase coexistence rules, and diffusion-based kinetics—into a unified optimization logic. The contribution of this work is theoretical rather than empirical. By articulating how physical constraints regularize learning, enhance interpretability, and support generalization under data scarcity, the framework advances applied artificial intelligence as a decision-relevant modeling paradigm for materials science. Implications for alloy design, model trustworthiness, and AI-assisted exploration of complex phase spaces are discussed.

Sequential learning is increasingly invoked in compositionally complex alloy modeling because the combinatorial scale of alloy design space, coupled with the cost of density-functional theory calculations and the staged nature of data acquisition, makes exhaustive one-shot training impractical. Yet under these conditions, neural networks are prone to catastrophic forgetting, whereby adaptation to newly introduced compositions, phases, or thermodynamic regimes degrades performance on previously learned ones. This article examines the conditions under which such failure emerges in compositionally complex alloys and shows that forgetting is not incidental but structurally induced by the interaction of shared representations, non-linear property landscapes, high compositional dimensionality, and sparse task-specific data. The analysis identifies four recurrent failure modes—elemental forgetting, concentration range collapse, phase space amnesia, and temperature-induced overwrite—and links them to underlying mechanisms of parameter interference, representation drift, output distribution shift, and gradient conflict. It further delineates the conditions that intensify these dynamics, including high compositional similarity, abrupt task transitions, rehearsal-free updates, and unstable optimization settings. By integrating detection criteria with mitigation strategies such as elastic weight consolidation, memory replay, modular architectures, and curriculum sequencing, the study provides a practical and conceptual framework for continual learning in alloy discovery. The central argument is that robust sequential learning in compositionally complex alloys requires forgetting-aware design at the levels of evaluation, architecture, and training protocol if materials machine learning is to support reliable lifelong modeling across expanding compositional spaces.

The term compositional generalization is increasingly invoked in machine learning for multi-principal element alloys (MPEAs), yet its meaning remains inconsistent and often conflated with interpolation or extrapolation. In standard machine learning, compositional generalization denotes the structured recombination of known components into novel configurations. Translating this concept to MPEAs is non-trivial due to their continuous composition space, permutation symmetries, and overlapping local atomic environments. Graph neural networks (GNNs), while effective for property prediction, are not inherently designed for such recombination beyond interpolation. This article identifies a resulting boundary problem and proposes an operational definition of compositional generalization based on four criteria: novel element–concentration co-occurrences, extrapolation beyond the convex hull of training compositions, controlled performance degradation, and permutation invariance. The framework clarifies widespread misuses of the term and provides a concrete basis for evaluating and designing models for MPEA discovery.

Machine learning interatomic potentials have become central tools in computational materials engineering, promising accurate and scalable predictions of alloy properties. Benchmarks in the field routinely report impressive accuracies, such as energy mean absolute errors below 20 meV/atom and low force root-mean-square errors, leading many papers to conclude that their models generalize well to alloy systems. Yet when these same potentials are deployed on real-world disordered alloys—such as binary solid solutions or multi-principal-element high-entropy alloys—the accuracy collapses, often by an order of magnitude. The root cause lies in the benchmarking pipeline itself: training and testing occur almost exclusively on ordered crystal structures drawn from databases of intermetallic compounds, while disordered configurations, which dominate practical applications, are never evaluated. This critical critique identifies four primary sources of systematic overestimation. First, the near-universal reliance on ordered training and test sets creates an illusion of generalization that fails the moment configurational disorder is introduced. Second, random or composition-based splits on ordered data allow models to memorize recurring structural motifs rather than learn transferable physics. Third, even when special quasirandom structures are employed as proxies for disorder, their periodic nature and limited sampling of local environments produce a misleading “SQS mirage.” Fourth, energy-only metrics mask severe force-prediction failures that render molecular-dynamics simulations unstable in disordered systems. These benchmarking flaws have concrete consequences: overconfidence among practitioners, misallocation of experimental resources, and slowed progress in high-entropy alloy design. By drawing on the literature, this article demonstrates that the ordered-to-disordered gap is not an edge case but a fundamental and under-reported limitation. It proposes concrete reforms—mandatory disordered test sets, configurational sampling protocols, and separate reporting of ordered versus disordered performance—to restore credibility to ML potential benchmarks. Until these changes are adopted, claims of “alloy-ready” potentials should be treated with skepticism. The field must move beyond ordered-crystal comfort zones if machine-learned potentials are to deliver on their promise for the compositionally complex materials that define modern materials science.

Ensemble methods have become the dominant framework for uncertainty quantification in machine learning models for high-entropy alloy (HEA) property prediction, where variance across independently trained neural networks is routinely interpreted as epistemic uncertainty. This metric now underpins active learning, compositional screening, and experimental decision-making, largely due to its simplicity and success in data-rich domains. This work shows that such reliance is fundamentally misplaced in HEAs. Ensemble variance implicitly assumes IID sampling, feature-space isotropy, uniform error, independence among models, and Gaussian residuals—conditions that are systematically violated in compositionally complex alloys. HEA datasets are biased toward equiatomic, stable compositions, the compositional manifold is anisotropic, predictive error is strongly heteroscedastic, ensemble members exhibit correlated failures, and extrapolation induces heavy-tailed errors. Under these conditions, ensemble variance becomes miscalibrated, underestimating uncertainty in sparse regions while overstating model reliability. The resulting distortions propagate through discovery workflows, yielding inefficient active-learning strategies, overconfident extrapolation, and misleading experimental guidance, even as larger ensembles appear to reduce uncertainty without improving accuracy. By linking these failures to their statistical origins, this paper clarifies why standard ensemble diversity is not a faithful proxy for epistemic uncertainty in HEAs. It further outlines diagnostics and targeted corrections that align uncertainty estimates with the physics and data structure of complex alloy systems, enabling more reliable and efficient materials discovery.

Integrating noisy, sparse, and heterogeneous experimental data with density functional theory (DFT) and machine learning (ML) is essential for reliable alloy design. While DFT enables high-throughput screening, its systematic biases limit predictive accuracy for real engineering alloys. This review synthesizes studies focused on fusing experimental measurements into ML–DFT workflows. We categorize five experimental data types—synthesis conditions, characterization data, property measurements, literature text, and industrial records—and identify six core challenges: noise, sparsity, heterogeneity, bias, missing metadata, and fragmentation. Six key integration strategies are examined: multi-fidelity learning, transfer learning, active learning with experimental feedback, Bayesian uncertainty modeling, multi-task learning, and physics-constrained augmentation. These approaches consistently reduce prediction errors by 30–60% compared to DFT-only models. Seven evidence-based best practices are distilled, emphasizing uncertainty reporting, data harmonization, experimental hold-out validation, and FAIR data sharing. Case studies demonstrate substantial gains in discovery efficiency for high-entropy alloys, superalloys, and phase diagrams. Remaining gaps, particularly the lack of standardized experimental databases and real-time feedback systems, are highlighted. This work provides a practical taxonomy and roadmap for developing experimentally grounded ML models that accelerate the design of high-performance alloys for aerospace, energy, and biomedical applications.