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The integration of surrogate modeling with high-throughput density functional theory (DFT) calculations has transformed materials discovery by enabling rapid screening of vast chemical spaces to predict properties. However, the inherent uncertainties in both DFT computations and surrogate approximations provide conceptual challenges to the reliability of screening results. This paper offers a conceptual reinterpretation of uncertainty in the screening of surrogate-driven materials and emphasizes how uncertainty reshapes the logic of discovery processes. We synthesize recent literature to highlight tensions between computational efficiency and predictive fidelity, where surrogate models approximate DFT data but introduce epistemic uncertainties from model simplifications and aleatory uncertainties from stochastic elements in ab initio simulations. By reframing uncertainty not merely as an error to minimize but as an informative signal guiding decision confidence, we argue for a paradigm in which uncertainty informs adaptive screening strategies, altering discovery trajectories toward more robust material identifications. This conceptual change emphasizes the need to integrate awareness of uncertainty into interpretive structures, fostering a nuanced understanding of how uncertainties propagate through screening paradigms. Ultimately, this perspective invites a critical examination of uncertainty’s role in bridging AI and DFT, promoting theoretical integration that enhances the interpretability and trustworthiness of AI-assisted materials discovery without relying on prescriptive frameworks.

Phonon spectra offer a uniquely demanding benchmark for graph neural network (GNN) force fields because they interrogate the second-derivative structure of the potential energy surface that governs lattice dynamics, thermal transport, and vibrational stability in materials. Yet direct comparison between GNN-derived and density functional theory (DFT) phonon spectra is frequently compromised by spectral leakage introduced through finite supercell truncation, displacement amplitude selection, q-point undersampling, Fourier interpolation, and post-processing broadening. These numerical effects can either conceal genuine deficiencies in the learned force field or generate apparent discrepancies that do not reflect model behavior. This article develops a hierarchical, leakage-aware validation framework that addresses this problem through progressive levels of scrutiny. The framework begins with baseline agreement in energies and forces, then advances to phonon density of states validation, q-resolved dispersion analysis, and finally a reproducible multi-metric assessment of spectral similarity. Progression through the hierarchy is conditional rather than automatic, such that higher-level claims are only made once lower-level numerical stability and model fidelity have been established. To separate methodological artifact from true representational error, the framework embeds explicit diagnostics based on supercell convergence, displacement sweeps, q-mesh refinement, interpolation cross-checks, and residual spectral analysis. It further introduces a standardized reporting protocol designed to make phonon-based validation transparent, comparable, and reproducible across studies of machine-learning interatomic potentials. By formalizing leakage control as an integral part of validation rather than an afterthought, the framework closes a critical gap between high-fidelity DFT phonon workflows and contemporary ML force-field development, enabling more credible assessment of GNN transferability in computational materials science.

Multi-fidelity machine learning has become a cornerstone of computational materials science because it leverages inexpensive low-fidelity data to accelerate training while reserving costly high-fidelity density functional theory (DFT) calculations for final refinement. Yet an often-overlooked source of uncertainty remains: DFT itself is noisy. Different choices of exchange-correlation functional, basis-set completeness, pseudopotential construction, and numerical convergence criteria introduce systematic and material-dependent errors that are routinely treated as exact labels. This theoretical analysis develops a unified conceptual framework for tracing how DFT error propagates through multi-fidelity training pipelines and ultimately inflates the variance of machine-learned predictions. The framework is grounded in recent theoretical and review literature on Gaussian-process and neural-network potentials, uncertainty quantification, and multi-fidelity surrogates. Proof sketches demonstrate the conditions under which multi-fidelity architectures reduce propagated variance and those under which they amplify it. Practical implications are drawn for uncertainty quantification protocols, optimal fidelity weighting, and the design of future multi-fidelity benchmarks. By making the DFT-to-ML noise pathway explicit, this work supplies a rigorous conceptual foundation for trustworthy data-driven materials modeling and highlights the necessity of reporting total (not merely model) uncertainty in high-stakes applications.

High-entropy alloy design requires exploring an enormous composition space in which conventional trial-and-error approaches are no longer viable. Density functional theory (DFT) delivers accurate formation energies and phase-stability predictions yet remains computationally prohibitive, yielding only sparse datasets of a few hundred structures per study. By contrast, the CALPHAD method furnishes abundant thermodynamic data across millions of compositions in seconds, yet it carries systematic biases when extrapolated beyond its binary and ternary calibration regimes. This conceptual framework presents a multi-fidelity learning strategy that systematically fuses sparse high-fidelity DFT data with abundant low-fidelity CALPHAD predictions to achieve near-DFT accuracy at CALPHAD-scale coverage. The framework rests on four tightly coupled components: a data-integration module that aligns CALPHAD and DFT outputs on identical compositions, a bias-correction module that learns the systematic mapping between the two fidelities, an uncertainty-propagation module that decomposes and combines fidelity-specific uncertainties, and an active-learning module that strategically selects the next DFT calculations where correction is most needed. An operational protocol translates these components into a repeatable workflow that begins with broad CALPHAD screening, proceeds through iterative DFT calibration, and converges when uncertainty falls below a designer-specified threshold. The approach reduces the DFT budget by approximately two orders of magnitude while preserving predictive fidelity, thereby opening previously inaccessible regions of high-entropy alloy space. Beyond immediate efficiency gains, the framework establishes a reusable blueprint for hybrid computational materials engineering in any system where abundant low-fidelity models coexist with sparse high-fidelity benchmarks. It therefore offers both a practical design pipeline for high-entropy alloys and a generalizable conceptual scaffold for multi-fidelity learning in complex concentrated alloys.