Consensus among machine learning models in materials artificial intelligence often manifests as aligned predictions across ensembles or diverse architectures, yet this alignment frequently conceals underlying misalignments in representational logic or epistemic foundations. This conceptual analysis interprets such phenomena through the lens of interaction dynamics between algorithmic assumptions, uncertainty propagations, and data-systemic interdependencies. By synthesizing insights from recent literature, the discussion illuminates how apparent harmonies in property predictions—such as electronic, mechanical, or thermal attributes—can emerge from shared artifacts rather than a coherent grasp of material phenomena. Analytical implications highlight steering logics in ensemble construction that trade diversity for stability, fostering feedback structures prone to amplifying spurious alignments. Epistemic reasoning underscores the interpretive tension between surface agreement and deeper validation, where consensus serves as an emergent indicator of systemic coherence or fragility. Ethical dimensions arise in the implications for knowledge production in materials discovery, urging nuanced scrutiny to discern integrative fidelity from illusory convergence. The framework advanced here conceptualizes consensus as a multifaceted interpretive construct, shaped by trade-offs in uncertainty handling and model diversity, thereby enriching understanding of AI’s role in reshaping materials’ conceptual landscapes. This approach advocates heightened epistemic vigilance, framing consensus not as a proxy for validation but as a dynamic site for probing the boundaries of interpretive reliability in data-driven materials inquiry.
The incorporation of artificial intelligence (AI) into materials science has precipitated a fundamental reconfiguration of how researchers conceptualize structure–property relationships, discovery pathways, and the epistemic status of computational predictions. Traditional materials research frameworks were historically grounded in mechanistic physical theories, phenomenological models, and iterative experimental refinement, where interpretive authority was closely tied to causal explanation and domain expertise. In contrast, contemporary AI-driven paradigms increasingly operate through high-dimensional inference, enabling predictions across expansive chemical, compositional, and configurational spaces that far exceed human tractability. By learning statistical regularities from curated datasets, these systems map structural descriptors, representations, or embeddings to material properties with remarkable apparent precision, reshaping expectations around speed, scale, and generality in materials discovery [1-8].
Within this evolving landscape, consensus—manifested when multiple models, architectures, or pipelines converge on similar predictions—has emerged as a powerful interpretive signal. Agreement among models is often treated as an indicator of robustness, reliability, or epistemic confidence, implicitly suggesting that convergent outputs reflect stable underlying material truths rather than contingent modeling artifacts [2, 4, 9-13]. In practical workflows, consensus plays a decisive role in prioritization, guiding candidate selection, allocating experimental resources, and shaping validation narratives in high-throughput screening campaigns. Yet, despite its centrality, the origins and implications of consensus in materials AI remain insufficiently interrogated, particularly when convergence arises not from complementary perspectives but from shared structural constraints embedded across models and data infrastructures.
Consensus in materials AI manifests across multiple operational contexts, including ensemble averaging for property estimation, agreement across distinct neural architectures, alignment between surrogate models and physics-based approximations, and concordance in iterative active learning loops [9, 10, 14, 15]. While such convergence is often celebrated as evidence of methodological rigor, interpretive challenges surface when agreement is driven by correlated limitations rather than independent lines of inference. Models trained on overlapping datasets, constructed around similar feature encodings, or optimized under analogous loss functions may exhibit convergence that reflects homogenized reasoning pathways rather than genuine corroboration of material behavior [5, 7, 16, 17]. In these cases, architectural diversity does not guarantee epistemic diversity, and ensemble consensus can amplify systemic bias rather than safeguard against it.
From a systems-level perspective, consensus emerges as an emergent property of interconnected socio-technical components rather than a purely algorithmic outcome. Datasets encode historical research priorities, experimental feasibility constraints, and representational gaps that shape what models can learn and what they systematically overlook. Algorithmic designs privilege certain invariances, symmetries, or smoothness assumptions that condition the space of admissible predictions. Uncertainty quantification strategies, while intended to characterize predictive confidence, may further cluster outputs around shared attractors without resolving deeper epistemic variance [13, 15, 18-23]. Together, these elements produce coordinated behaviors across models, giving rise to a consensus that is structurally induced rather than epistemically earned.
The implications of such consensus are particularly salient in domains of materials innovation where exploration is costly, irreversible, or normatively charged. In areas such as energy materials, semiconductors, catalysis, or advanced alloys, aligned predictions frequently serve as steering signals that guide exploration toward ostensibly promising regions of material space. Overreliance on consensual outputs, however, risks channeling effort into zones of illusory stability, where agreement masks unresolved uncertainties or unexamined assumptions embedded in data and representations [6, 19, 24]. In this sense, consensus does not merely reflect knowledge—it actively shapes discovery trajectories, reinforcing certain pathways while foreclosing others.
Epistemic considerations further complicate the interpretive status of consensus, especially under the data-scarce conditions that characterize many materials science problems. Unlike domains with abundant observational data, materials contexts often involve sparse sampling, expensive measurements, and incomplete coverage of relevant physical regimes. When multiple models converge on predictions for underrepresented phenomena—such as defect energetics, metastable phases, or non-equilibrium transformations—agreement may signify the propagation of shared uncertainty rather than integrative understanding [2, 9, 20]. Consensus in these regimes risks being misinterpreted as validation when it actually reflects the absence of constraining evidence.
These dynamics introduce ethical dimensions into the interpretation of consensus, particularly in relation to responsible AI deployment and resource governance. Misplaced confidence in aligned predictions can skew prioritization toward certain material classes or design strategies, shaping funding decisions, experimental agendas, and sustainability claims [12, 25-27]. When consensus is treated as epistemic closure rather than a provisional signal, it may obscure the need for reflexive scrutiny, reinforcing narratives of certainty that outpace underlying knowledge. Ethical responsibility thus extends beyond model performance to include how agreement is interpreted, communicated, and operationalized within scientific and institutional contexts.
At the level of model ecosystems, consensus is entwined with trade-offs that resist simple resolution. Increasing ensemble diversity to probe disagreement may impose substantial computational and organizational costs, while enforcing tighter alignment across models can streamline workflows at the expense of epistemic pluralism [8, 14, 21]. Iterative feedback structures—such as active learning, closed-loop experimentation, or adaptive sampling—further intensify these tensions. In such systems, consensual signals often guide model refinement and data acquisition. Yet, the same signals can entrench existing representational biases, creating self-reinforcing loops that stabilize convergence without enhancing understanding [3, 10, 17]. These patterns echo historical precedents in theoretical modeling, where distinct formalisms yield similar macroscopic predictions despite divergent microscopic assumptions—now amplified by AI’s capacity for scale, speed, and parallel inference [4, 22].
Contemporary efforts to mitigate these risks increasingly turn to hybrid frameworks that integrate physics-informed constraints, domain knowledge, or symbolic priors into machine learning architectures. While such approaches aim to anchor consensus in fundamental principles, interpretive scrutiny reveals that misaligned convergence can persist if constraints inadequately restrict latent spaces or are themselves encoded in simplified forms [15, 18, 23]. Physics-informed consensus, like purely data-driven agreement, remains susceptible to overinterpretation when the relationship between constraint satisfaction and epistemic validity is left implicit.
Against this backdrop, this manuscript positions consensus as a conceptual pivot in materials AI—neither inherently virtuous nor inherently misleading, but deeply contingent on the structures that produce it and the interpretations that follow. By examining consensus across algorithmic, epistemic, and systemic dimensions, the analysis seeks to illuminate how convergence emerges, how it acquires authority, and how it can be more critically interpreted. Rather than rejecting consensus as a signal, this work argues for a more discerning engagement with its dynamics, fostering interpretive practices that balance efficiency with epistemic humility in the pursuit of AI-driven materials discovery.
Conceptual evolution of consensus in materials AI: The notion of consensus has evolved alongside the maturation of machine learning applications in materials science, transitioning from simple averaging techniques to sophisticated ensemble methods that treat agreement as an indicator of robustness [1, 3, 8]. Early conceptualizations viewed consensus primarily as a means of predictive enhancement. Yet, recent syntheses interpret it as revealing interaction dynamics between model architectures and data regimes, where alignment may signify shared inductive biases rather than comprehensive fidelity [2, 4, 13]. In property prediction contexts, such as electronic band structures or mechanical responses, ensembles often exhibit convergence that analytical lenses attribute to correlated feature sensitivities or distributional overlaps [9, 17, 21].
The uncertainty quantification literature enriches this evolution by framing consensus amid probabilistic spreads, where clustered outputs reflect epistemic alignment or divergence [10, 13, 15]. Systems insights depict feedback loops in which uncertainty propagation modulates agreement, potentially masking underlying dissonances in scenarios of sparse or noisy data [2, 23, 27].
Ensemble dynamics and interpretive tensions: Ensemble approaches introduce interpretive tensions arising from diversity-stability trade-offs. Conceptual analyses portray ensembles as networks of interacting logics, where variance in training or architecture aims to expose misalignments. Yet, homogenization pressures—stemming from shared data pipelines or optimization objectives—can foster consensual artifacts [5, 7, 12]. In graph-based representations of crystalline or molecular systems, agreement among members might stem from topological emphases that overlook subtler chemical nuances [18, 22]. Steering logics in ensemble design thus balance exploratory breadth against convergence risks, with ethical reasoning highlighting accountability in interpretations that inform discovery trajectories [11, 16].
Integrative views emphasize feedback in iterative ensembles, where retraining on consensual regions may entrench patterns, while disagreement probing offers pathways to epistemic refinement [6, 14, 17].
Data regimes and uncertainty in consensus formation: Data characteristics profoundly shape interpretations of consensus. Synthesizing works, biases, or gaps in materials datasets—such as the underrepresentation of metastable phases—induces alignments rooted in artifactual regularities [19, 23, 24]. Uncertainty dynamics interact here, as models calibrated on analogous noise profiles converge harmoniously yet reveal epistemic voids upon closer scrutiny [2, 13, 20]. Trade-offs between data scale and quality steer consensus toward apparent coherence in high-volume regimes, while sparsity accentuates fragility [25, 27]. Hybrid frameworks incorporating domain constraints attempt to ground consensus, but feedback from data-driven elements can introduce persistent interpretive misalignments [15, 17].
Epistemic and ethical intersections: Epistemic dimensions position consensus within broader knowledge narratives, where misinterpreted agreement perpetuates loops of reinforced assumptions across model generations [4, 9, 26]. Ethical reasoning advocates interpretive transparency to align consensus with accountability principles, particularly as AI influences materials innovation priorities [11, 12]. Human-AI interaction dynamics add complexity, with overdependence on consensual outputs potentially diminishing critical oversight [3, 10]. Trade-offs between acceleration and depth underscore the need for resilient interpretive strategies [6, 16].
Integrative views on feedback structures and trade-offs: Integrative syntheses conceptualize consensus through feedback architectures and inherent trade-offs. Iterative cycles can either expose or conceal misaligned reasoning, modulated by designs that favor certain pathways [18, 21, 22]. In inverse design paradigms, consensual candidates may reflect optimization constraints more than exhaustive exploration [19, 23]. Ethical lenses urge the incorporation of diverse perspectives to bolster interpretive resilience [5, 8]. Collectively, the literature portrays consensus as an emergent interpretive phenomenon, interwoven with systemic, epistemic, and ethical threads [1, 7, 20, 24, 27]. To clarify the epistemic heterogeneity underlying apparent agreement, Table 1 synthesizes distinct modes of model consensus in materials AI, linking sources of alignment to uncertainty behavior, systemic feedback, and interpretive risk. This typology illustrates how agreement may arise from integrative coherence or, conversely, from correlated artifacts that render consensus epistemically fragile.
Table 1. Modes of model consensus in materials AI and their epistemic significance
Consensus mode | Primary source of agreement | Underlying interaction dynamics | Typical uncertainty signature | Epistemic risk profile | Systems-level consequences | Interpretive implication |
Integrative alignment | Complementary representations across heterogeneous models | Diverse architectures converge through materially grounded constraints (e.g., physics-informed priors, distinct feature abstractions) | Heterogeneous but overlapping uncertainty estimates | Low | Broadens exploration while preserving interpretive robustness | Consensus reflects coherent material understanding rather than surface agreement |
Correlated inductive bias | Shared feature encodings or training distributions | Homogenized learning pathways across nominally distinct models | Narrow, clustered uncertainty bands | High | Reinforces representational blind spots | Agreement masks epistemic redundancy rather than independent validation |
Optimization-induced harmony | Common loss functions and benchmarking objectives | Convergence driven by optimization pressures rather than material reasoning | Artificially suppressed epistemic variance | High | Channels discovery toward illusory optima | Consensus reflects algorithmic convenience, not material truth |
Data-regime convergence | Sparse or skewed datasets | Alignment emerges from shared data gaps or underrepresentation | Uniform uncertainty inflated or redistributed | Medium–High | Narrow the exploratory scope under data scarcity | Agreement signals shared ignorance rather than shared insight |
Uncertainty compression | Aggregation or averaging strategies | Uncertainty is smoothed out during ensemble fusion | Reduced apparent variance despite epistemic gaps | High | Produces false confidence in predictions | Consensus arises through epistemic flattening |
Feedback-reinforced consensus | Iterative retraining on consensual regions | Active learning loops amplify early alignments | Progressively shrinking uncertainty | Very High | Entrenches early artifacts across the pipeline | Agreement becomes self-justifying and brittle |
Constraint-driven consensus | Physics-informed or rule-based constraints | Partial anchoring in physical principles with latent flexibility | Structured but potentially misleading uncertainty | Medium | Stabilizes outputs while hiding latent misalignment | Consensus appears principled but may remain underdetermined |
Surface-level agreement | Output similarity without representational coherence | Models agree numerically but diverge internally | Inconsistent uncertainty attribution | Medium | Encourages premature closure | Agreement lacks diagnostic depth |
Wrong-reason consensus | Correlated biases and shared sensitivities | Agreement driven by artifacts, priors, or optimization shortcuts | Stable yet uninformative uncertainty patterns | Very High | Misguides prioritization and resource allocation | Consensus is epistemically brittle despite apparent robustness |
Productive dissonance (counterfactual) | Managed disagreement across models | Deliberate diversity reveals epistemic boundaries | Divergent but interpretable uncertainty | Low (desirable) | Expands hypothesis space | Lack of consensus functions as an epistemic signal |
This framework conceptualizes consensus in materials AI not as evidentiary confirmation, but as an emergent interpretive nexus arising from layered interactions among algorithmic substrates, uncertainty mediations, and epistemic contexts. Rather than treating agreement as an endpoint, the framework positions consensus as a relational phenomenon—one that reflects the alignment of surface-level outputs while potentially concealing subsurface computational logics that generate reinforcement, amplification, or tension.
At the core of this interpretation is the distinction between observable convergence and generative coherence. Ensemble or multi-model agreement is understood as the interface where model architectures, training regimes, and representational priors intersect, producing apparent harmony that may signal either systemic robustness or latent fragility. Consensus thus becomes diagnostically meaningful only when situated within the interaction structures that produced it, rather than interpreted as an intrinsic indicator of correctness.
Uncertainty functions as a primary mediating layer within this framework. Clustered predictions are interpreted as sites where aleatoric variability and epistemic indeterminacy interact through feedback loops embedded in training data, loss functions, and validation protocols. These loops may stabilize illusory agreement—where uncertainty is redistributed or suppressed without genuine resolution—or surface productive dissonance that reveals epistemic boundaries. Consensus, in this sense, is shaped not only by prediction alignment but by how uncertainty is encoded, propagated, and interpreted across models.
A second axis of the framework concerns diversity trade-offs. Model heterogeneity—architectural, parametric, or data-induced—is conceptualized as a steering mechanism that modulates the epistemic weight of consensus. Low effective diversity can produce tightly clustered outputs that reflect shared representational shortcuts rather than independent material reasoning, while excessive divergence may fragment interpretive coherence. The framework interprets diversity not as an intrinsic virtue, but as a calibrated condition that determines whether consensus functions as integrative synthesis or premature closure.
These dynamics are embedded within broader epistemic layering, where consensus participates in validation narratives shaped by institutional norms, benchmarking practices, and ethical commitments. From this perspective, consensus is not merely computational; it is co-produced by scientific expectations, evaluation cultures, and governance structures that influence which alignments are amplified, trusted, or operationalized. Ethical reasoning enters the framework by foregrounding responsibility in knowledge stewardship, particularly where consensual outputs guide resource allocation, experimental prioritization, or claims of discovery under conditions of sparse ground truth.
At the systems level, the framework situates consensus within interconnected materials AI ecosystems—datasets, model families, workflow protocols, and feedback channels—whose interactions govern the emergence of authentic versus spurious harmonies. Of particular concern is wrong-reason consensus: scenarios in which outputs align due to correlated biases, shared parametric sensitivities, or common optimization pressures, rather than convergent material understanding. Such consensus appears stable at the surface while remaining epistemically brittle beneath, encouraging holistic reasoning that links micro-level model behavior to macro-level consequences for materials knowledge production. As shown in the inner ring of Figure 1, reinforcing feedback within ‘Algorithmic Substrates’—such as feature encoding and architectural priors—can initiate pathways that later manifest as outcomes in the outermost ring.
Figure 1. A layered schematic of consensus dynamics in materials AI. The diagram depicts a central “Consensus Nexus” interacting with nested rings of Algorithmic Substrates, Uncertainty Mediations, and Epistemic Contexts, illustrating pathways toward either Spurious Harmony or Integrative Alignment.
The proposed framework yields several analytical implications that collectively reorient how consensus is interpreted within materials AI. Rather than treating agreement as an endpoint or a proxy for validation, these implications reposition consensus as a diagnostic phenomenon whose internal structure, mediating conditions, and systemic effects warrant interpretive examination.
Within this framework, agreement among models is reframed from a confirmatory outcome to a diagnostic site. Convergence is no longer interpreted as self-evident support for predictive validity, but as an occasion to interrogate the interaction pathways that generated alignment. Analytical attention shifts toward examining whether consensus emerges from genuinely complementary material reasoning across models, or from shared representational shortcuts embedded in training data, architectural priors, or optimization landscapes.
This distinction carries important analytical consequences. Surface-level harmony may conceal deep homogeneity in feature spaces or loss sensitivities, producing agreement that reflects correlated inductive biases rather than independent inferential support. By treating consensus as an object of scrutiny, the framework encourages analytical practices that trace agreement backward through model interactions, revealing whether convergence signifies integrative coherence or latent fragility. Consensus thus becomes a signal whose meaning is contingent on the generative processes that sustain it, rather than a stopping criterion that forecloses further reasoning.
A second implication concerns the interpretive role of uncertainty. Rather than treating uncertainty solely as a quantitative boundary or confidence measure, the framework positions uncertainty as a mediating signal that shapes how consensus should be interpreted. Divergent uncertainty profiles underlying convergent predictions are analytically significant, indicating unresolved epistemic tension masked by output alignment.
From this perspective, agreement accompanied by suppressed or homogenized uncertainty may signal epistemic compression rather than epistemic resolution. Analytical focus, therefore, shifts toward understanding how data regimes, training procedures, and algorithmic priors modulate uncertainty propagation across models. Where uncertainty is unevenly distributed or selectively dampened, apparent stability may arise not from shared understanding of material behavior, but from structural constraints that limit disagreement. This reframing elevates uncertainty from a secondary annotation to a central interpretive axis in evaluating consensus.
The framework further reframes diversity within model ensembles as an analytical probe, rather than an intrinsic guarantee of epistemic robustness. Nominal heterogeneity—differences in architecture, initialization, or training subsets—may coexist with low effective diversity if steering logics promote homogenization through shared datasets, common evaluation benchmarks, or convergent optimization pressures.
Analytically, this implies that ensemble agreement cannot be interpreted independently of the interaction structures that shape model behavior. Low variance in outputs may reflect coordinated exploration of material space, but it may equally reflect premature collapse onto dominant representational modes. The framework thus cautions against equating ensemble size or architectural plurality with epistemic independence, instead encouraging analyses that examine how diversity is operationalized, constrained, or neutralized within materials AI workflows.
At the systems level, the framework highlights how consensus participates in feedback-driven dynamics that can entrench early alignments. Iterative refinement pipelines—common in materials screening, surrogate modeling, and active learning—often reuse consensual outputs to guide subsequent exploration, model retraining, or experimental prioritization. While such feedback can enhance efficiency, it also risks creating closed interpretive cycles in which initial alignments are progressively reinforced rather than critically examined.
The analytical implication is that consensus must be situated within its feedback structures. An agreement that persists across repeated cycles may reflect the stabilization of artifacts rather than the accumulation of insight. Without interpretive checkpoints, consensus can become self-justifying, narrowing exploration and suppressing alternative material hypotheses. This systems-level perspective reframes consensus as a dynamic participant in knowledge production, whose persistence may signal fragility as much as robustness.
Finally, the framework foregrounds the epistemic and ethical stakes of consensual outputs in materials AI. Consensus is interpreted as a contingent, constructed phenomenon whose epistemic authority cannot be inferred from alignment strength alone—particularly in domains characterized by sparse, indirect, or proxy ground truth. An agreement may stabilize narratives of discovery that exceed the evidentiary capacity of the underlying models.
Ethical reasoning amplifies this implication by highlighting how uncritical reliance on consensus can shape scientific priorities, funding decisions, and exploratory trajectories. When consensual signals are treated as authoritative, they may divert attention to regions of spurious promise while marginalizing less-explored but potentially significant material spaces. Over time, such dynamics risk narrowing the epistemic horizon of materials innovation, embedding biases that are difficult to detect or reverse.
The conceptual analysis presented here engages with the multifaceted nature of model consensus in materials AI, foregrounding interpretive tensions that arise when agreement conceals misaligned foundations. By synthesizing recent literature and advancing an integrative framework, the discussion has sought to illuminate consensus not as a straightforward marker of reliability but as a relational construct shaped by algorithmic substrates, uncertainty mediations, epistemic contexts, and feedback dynamics [1, 3, 9, 13].
Central to this engagement is the recognition that consensus often reflects systemic interdependencies rather than isolated model performance. Ensemble approaches, uncertainty quantification strategies, and data-driven workflows interact in ways that can produce harmonious outputs from disparate or even conflicting reasoning pathways [2, 10, 15, 17]. This interpretive stance aligns with literature emphasizing the epistemic challenges of interpreting aggregated predictions, particularly in regimes characterized by data sparsity, distributional shifts, or underrepresented material classes [19, 23, 24, 27].
The framework’s emphasis on steering logics and trade-offs offers a conceptual scaffold for navigating these challenges. Diversity-stability balances, feedback reinforcement mechanisms, and uncertainty propagation paths emerge as recurring motifs that modulate the character of consensus [7, 8, 12, 21]. Where literature has documented ensemble benefits in predictive tasks, the present analysis interprets these benefits within a broader epistemic landscape, questioning the conditions under which apparent gains reflect genuine integration versus artifactual convergence [4, 18, 22, 28-30].
Ethical and responsibility considerations permeate the discussion. As AI increasingly informs decisions in materials design—ranging from energy storage to structural alloys—misplaced interpretive weight on consensual outputs carries consequences for research trajectories, resource commitment, and long-term innovation equity [5, 11, 16, 26]. The framework, therefore, implicitly advocates interpretive vigilance: practices that routinely probe the origins and coherence of agreement rather than relying on consensus as proxy validation [6, 14, 25, 29-32].
Limitations of the present conceptual approach naturally follow from its deliberate abstraction. By remaining strictly non-empirical, the analysis forgoes direct illustration through case studies or quantitative benchmarks, focusing instead on integrative patterns observable across diverse contributions [1, 3, 13, 20, 30-34]. This abstraction enables generality but defers concrete operationalization to future interpretive or methodological developments.
The discussion ultimately positions consensus as a conceptual frontier in materials AI—one that invites ongoing reflection on how data-driven systems reshape scientific reasoning. By foregrounding interaction dynamics, epistemic layering, and systemic feedback, the framework contributes a nuanced lens through which researchers may interrogate agreements that appear robust yet rest on fragile interpretive grounds.
This conceptual analysis has explored the phenomenon of model consensus in materials artificial intelligence through an interpretive and integrative lens, emphasizing the complex origins and implications of apparent agreement among predictive systems. Consensus emerges not as a simple indicator of fidelity but as a dynamic outcome shaped by algorithmic assumptions, interactions with uncertainty, data-systemic constraints, and feedback structures. The proposed framework conceptualizes these elements as layered and interrelated, offering analytical pathways to distinguish integrative alignment from artifactual harmony.
By synthesizing recent scholarly insights and foregrounding epistemic reasoning, trade-offs, and steering logics, the work highlights the interpretive demands placed upon researchers who rely on AI-generated consensus in materials discovery. Ethical reflections underscore the responsibility to maintain vigilance over consensual outputs, ensuring that their influence on scientific and technological trajectories aligns with principles of coherence and accountability.
Ultimately, the analysis reframes consensus as an emergent interpretive construct—one that invites continuous scrutiny of the boundaries between surface agreement and deeper understanding in data-driven materials inquiry. This perspective contributes to the maturation of materials AI as a domain of conceptual and computational innovation, where discerning the reasons behind model agreement becomes integral to advancing reliable and responsible knowledge production.
None
None
None
None
Open Access The author(s) retain copyright. This article is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It may be shared and adapted for non-commercial purposes with appropriate attribution, an indication of changes, and distribution of adaptations under the same license. Third-party material may be subject to separate terms identified in its credit line. View the license at https://creativecommons.org/licenses/by-nc-sa/4.0/.
Institute for Advanced Materials Research Press
