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The integration of artificial intelligence into materials science has highlighted challenges in model performance, particularly in domains that require extrapolation beyond the training data distribution. This manuscript explores compositional generalization as a unique failure mode in materials AI, in which systems struggle to interpret novel combinations of atomic or molecular elements despite familiarity with individual components. Through a synthesis of recent literature, the analysis delineates how this failure manifests in predictive tasks, such as property estimation in alloys or polymers, revealing underlying tensions between data-driven learning and structural comprehension. Conceptual interpretations highlight the interplay between representational invariance and contextual dependencies, underscoring epistemic gaps in current architectures. The proposed framework interprets these dynamics through lenses of modular interaction and systemic feedback, emphasizing trade-offs in scalability and robustness. By examining the ethical ramifications of deployment in high-stakes applications, the discussion integrates insights into steering mechanisms that could mitigate such limitations without empirical validation. Ultimately, this conceptual inquiry fosters a deeper understanding of AI’s role in advancing materials discovery and advocates for interpretive strategies that prioritize holistic integration over isolated optimizations.

In the rapidly evolving intersection of artificial intelligence (AI) and materials science, interpretability techniques promise to bridge computational predictions with scientific understanding. This manuscript proposes a novel conceptual framework that reconceptualizes interpretability as a process of scientific translation, wherein AI outputs are systematically mapped onto material mechanisms. We define AI outputs as encompassing feature attributions, counterfactuals, attention- or saliency-style signals, latent representations/embeddings, surrogate trends, and natural-language rationales. Materials mechanisms, in turn, are formalized as entities and causal relations across atomic/defect chemistry, phase stability/transformations, diffusion/transport, microstructure evolution, and processing–structure–property linkages. The framework addresses the explanation gap by arguing that raw interpretability signals do not inherently constitute mechanistic explanations, particularly in materials science, where multi-scale complexities amplify translation challenges. Through a stepwise translation model, we introduce validity gates—such as scope delimitation, identifiability checks, invariance assessments, causal plausibility evaluations, and scale consistency verifications—to ensure rigorous mapping from AI signals to mechanistic claims. This approach theorizes translation failure modes, including proxy misalignments, confounding interferences, domain shifts, scale mismatches, and narrative overreaches, and delineates strategies to contain them. By synthesizing prior typologies of AI outputs and mechanistic constructs in materials, the framework advances a structured pathway for deriving legitimate scientific insights from AI, fostering theoretical progress in applied AI for materials discovery without empirical validation.

Artificial intelligence (AI) is increasingly embedded across the materials design lifecycle. Yet, prevailing approaches to trustworthiness remain largely model-centric, emphasizing predictive accuracy while under-specifying how AI outputs translate into high-stakes material decisions. This limitation is particularly consequential in materials science, where decisions frequently commit resources to irreversible synthesis, deployment, and long-term societal or environmental impact. Here, we propose a novel decision-centric conceptual framework for trustworthy AI in materials design, defining trustworthiness as the justification of action recommendations under uncertainty—including decisions to select, reject, prioritize, stop, or redesign candidate materials—rather than as an intrinsic property of models alone. The framework structures the materials lifecycle as an iterative sequence of seven decision-bearing stages—from problem framing to revision—and introduces five validity gates—scope, domain, uncertainty, consequence, and sustainability—that serve as systematic filters between AI outputs and actionable commitments. Trust dimensions such as reliability, robustness, transparency, accountability, safety, and sustainability are conceptualized as emergent properties of gated lifecycle interactions rather than isolated criteria. By identifying where failures originate across the lifecycle and formalizing named failure modes with corresponding containment principles, the framework explicitly links uncertainty quantification, interpretability, and governance considerations to defensible decision-making in materials contexts. This work provides a unifying theoretical structure for understanding how trustworthy AI decisions can be operationalized in materials design, offering conceptual grounding for future methodological, institutional, and governance advances in applied artificial intelligence for materials science.

The growing integration of artificial intelligence (AI) into materials science has substantially accelerated materials discovery and property prediction. Yet, the explanations produced by these systems often exhibit systematic failures that undermine their epistemic reliability. Despite increased attention to explainable AI, existing studies address explanation shortcomings in a fragmented, tool-centric manner, leaving unresolved questions about their scientific legitimacy. This conceptual manuscript introduces a unified theoretical framework for understanding failure modes in materials AI explanations as emergent properties of interaction dynamics between algorithmic representations, data ontologies, and domain epistemologies. Synthesizing literature, we identify three recurrent clusters of explanation failure—representational distortions, inferential misalignments, and contextual dissonances—each arising from structural trade-offs in model design, training, and deployment. To address these challenges, we articulate prevention principles as steering logics that operate through feedback structures, enabling recalibration of explanations without constraining predictive performance. Analytical implications demonstrate how explanation failures influence interpretive confidence, knowledge production, and ethical decision-making across materials research workflows. By reframing explanation failure as a diagnostic signal rather than a technical defect, the framework advances a systems-level understanding of AI explanations. It provides conceptual guidance for cultivating more trustworthy and epistemically aligned AI practices in materials science.

Rotational invariance, a core design principle in graph neural networks (GNNs) for materials property prediction, has enabled remarkable accuracy for scalar quantities such as formation energy and band gap yet becomes a fundamental liability when the target property is a tensor of rank two or higher. Anisotropic elastic constants, which describe the directional stiffness of crystals and enter engineering design as a 6×6 Voigt matrix with up to 21 independent components in triclinic systems, must transform covariantly under rotation; any architecture that collapses all orientational information into a single invariant scalar cannot, in principle, recover the required tensorial structure. This failure-mode analysis articulates four distinct mechanisms by which invariance induces systematic error—diagonal indistinguishability, off-diagonal blindness, shear underspecification, and symmetry-induced degeneracy—and demonstrates how each arises directly from the interplay of global pooling, distance-only edge features, and symmetry averaging that characterize rotationally invariant models such as CGCNN, MEGNet, and SchNet. Detection principles based on equality tests, anisotropy-ratio checks, and differential error audits provide practitioners with practical diagnostics, while mitigation principles centered on equivariant architectures, directional encoding, and stress-tensor multi-task learning chart a clear path toward reliable tensorial prediction. The implications extend beyond elasticity to any directional property in low-symmetry crystals and call for a principled redesign of materials GNNs that treats invariance as a deliberate choice rather than a default.

Machine learning molecular dynamics has become a cornerstone for exploring solid-state electrolytes in next-generation batteries. Researchers rely on these potentials to predict ionic conductivity, lithium diffusion, and phase behavior at speeds far beyond traditional density functional theory while maintaining near-DFT accuracy. Yet a critical gap remains: most ML potentials are trained exclusively on static, zero-kelvin structures and energies. When deployed in finite-temperature simulations between 300 K and 1000 K—the actual operating regime of solid-state electrolytes—hidden instabilities emerge that standard benchmarks never detect. This failure-mode analysis identifies five specific instabilities that compromise the reliability of ML-driven molecular dynamics for solid-state electrolytes. Energy drift in long NVT or NVE runs violates conservation laws and produces artificial heating or cooling. Unphysical lithium diffusion pathways appear because transition states and saddle-point configurations are absent from training data, leading to either barrierless motion or spurious trapping. Force discontinuities arise from non-smooth descriptor cutoffs and become amplified by thermal motion. Phonon softening is mispredicted, distorting the vibrational precursors to superionic transitions. Finally, the superionic transition itself is either shifted by more than 100 K, entirely absent, or incorrectly sharp or gradual. These instabilities are invisible in conventional zero-kelvin tests such as energy mean-absolute error or force root-mean-square error on equilibrium structures. They only surface during extended nanosecond-scale simulations at operating temperatures. The present work systematically dissects why each failure mode occurs, provides clear detection signatures observable in any ML molecular-dynamics workflow, and outlines practical mitigation strategies grounded in the literature. By treating finite-temperature stability as a core validation requirement rather than an afterthought, the field can move from promising prototypes to trustworthy tools for solid-state electrolyte design. The analysis draws on recent advances in machine-learning interatomic potentials while highlighting the urgent need for temperature-aware training and testing protocols.

Piezoelectricity is a tensor property of rank 3 that couples mechanical stress to electric polarization in non-centrosymmetric crystals. Accurate prediction of the piezoelectric tensor from atomic structure is essential for discovering new sensors, actuators, and energy-harvesting materials in data-driven materials engineering. Yet many researchers continue to apply invariant graph neural networks such as CGCNN, MEGNet, and SchNet—architectures originally designed for scalar properties like formation energy—to this tensor task. These models are rotationally invariant: their outputs remain unchanged under any reorientation of the crystal lattice. The piezoelectric tensor, however, must transform covariantly with orientation. As a direct consequence, invariant GNNs systematically predict zero piezoelectricity or near-zero random noise across entire datasets. This failure mode analysis identifies four core reasons: sign ambiguity arising from untracked orientation-dependent sign flips, component confusion caused by rotational permutation of tensor elements, symmetry overconstraint that forces unphysical relations across crystal classes, and vanishing off-diagonal components that average to zero under random rotations. The analysis draws exclusively on peer-reviewed literature in computational materials science and shows that equivariant GNNs are not merely an improvement but a strict necessity for any rank-2 or rank-3 tensor property. Detection principles and mitigation strategies are outlined conceptually, without reference to specific benchmarks or numerical scores. The work underscores a broader limitation in invariant architectures for tensorial phenomena in materials informatics and calls for their replacement by orientation-aware equivariant frameworks in future tensor property prediction pipelines.