Property Prediction vs Mechanistic Insight: A Conceptual Divide in Materials AI

In computational materials engineering, the integration of artificial intelligence (AI) has transformed discovery pipelines from labor-intensive simulations to data-driven infrastructures capable of navigating vast chemical spaces. High-throughput computations and machine learning architectures, such as graph neural networks, have enabled rapid property prediction, accelerating the screening of candidates for applications ranging from energy storage to structural alloys. Yet, this paradigm emphasizes forward modeling—mapping inputs to outputs—often at the expense of mechanistic insight, which requires disentangling causal interactions within atomic-scale dynamics. The conceptual divide between property prediction and mechanistic insight manifests in epistemic tensions: predictive models excel in interpolation but falter in extrapolation, while insight-oriented approaches demand representations that encode not just structural motifs but relational hierarchies across scales. This manuscript introduces the Interpretive Cascade Framework, a systems-level conceptualization that reframes materials AI as a layered cascade of representation, inference, and steering logics. By integrating multimodal data streams with feedback-mediated discovery workflows, the framework elucidates how computational infrastructures can balance predictive efficiency with interpretive depth, mitigating risks of epistemic opacity in closed-loop experimentation. Structural layers delineate data ingestion to hypothesis refinement, incorporating uncertainty propagation as a steering mechanism rather than a mere byproduct. Implications for the field lie in reorienting AI ecosystems toward hybrid discovery logics, where representation learning informs inverse design without sacrificing traceability. This interpretive lens fosters resilient infrastructures, enabling materials science to evolve beyond black-box predictions toward epistemically robust computational paradigms that sustain long-term innovation in data-driven materials engineering.

The evolution of computational materials engineering over the past decade has been marked by a profound shift toward data-centric methodologies, where artificial intelligence (AI) serves as the linchpin for integrating disparate simulation outputs into cohesive discovery ecosystems [1, 2]. Traditionally, materials design relied on density functional theory (DFT) calculations and molecular dynamics to probe electronic structures and thermodynamic stabilities, a process inherently constrained by computational expense and the combinatorial explosion of chemical compositions [3]. The advent of high-throughput infrastructures has alleviated these bottlenecks, automating workflows that generate terabytes of property data across alloy phases, perovskites, and metal-organic frameworks [4, 5]. Platforms like Materials Project and AFLOW exemplify this scalability, curating open-access repositories that fuel machine learning (ML) models for bandgap estimation, elastic moduli prediction, and thermal conductivity forecasting [6, 7].

At the core of this transformation lies the materials informatics paradigm, which posits data as the primary currency for scientific inference [1, 8]. ML algorithms, particularly those leveraging convolutional and recurrent architectures, have demonstrated prowess in distilling patterns from noisy, high-dimensional datasets, enabling virtual screening of synthesis parameters for inorganic solids [9, 10]. Graph neural networks (GNNs), with their capacity to encode atomic connectivities as relational graphs, further enhance this by capturing non-local interactions in crystalline lattices [11, 12]. Such architectures underpin autonomous discovery systems, where reinforcement learning agents iteratively refine experimental protocols in closed-loop setups, bridging simulation and laboratory validation [13, 14]. This synergy has democratized access to predictive tools, allowing researchers to prioritize promising candidates in inverse design scenarios, such as tailoring electrocatalysts for hydrogen evolution [15, 16].

Yet, the dominance of predictive paradigms introduces subtle epistemic frictions within these ecosystems. Property prediction, while efficient for ranking hypotheses, operates predominantly in a forward-mapping regime: inputs (e.g., elemental compositions or structural descriptors) are transformed into outputs (e.g., formation energies) via learned mappings that prioritize generalization over explication [17, 18]. This black-box orientation—evident in the widespread adoption of ensemble regressors and deep neural potentials—excels in interpolative regimes but exposes vulnerabilities in sparse data landscapes, where extrapolation demands causal reasoning beyond mere correlation [19, 20]. For instance, in high-entropy alloys, ML-driven screening identifies low-energy configurations but rarely elucidates the entropic contributions to phase stability, leaving mechanistic voids that hinder transferability across compositional spaces [21, 22].

These limitations are compounded by the representational underpinnings of current AI frameworks. Descriptors in materials ML, from Coulomb matrices to SOAP kernels, encode geometric and electronic features but often neglect hierarchical interdependencies, such as how defects propagate through grain boundaries or how solvation shells modulate interfacial energetics [23, 24]. Representation learning, while advancing through autoencoders and variational inference, remains tethered to supervised objectives that optimize for accuracy rather than interpretability [15, 25]. Consequently, the inference layer in discovery pipelines—where models generate actionable insights—suffers from epistemic opacity, as uncertainty quantification (UQ) is retrofitted post-hoc rather than woven into the representational fabric [26, 27]. In multimodal datasets amalgamating DFT, experimental spectra, and microstructural images, this disconnect manifests as fragmented knowledge graphs, impeding the holistic synthesis required for robust materials design [6, 28].

High-throughput computation, for all its virtues, amplifies these constraints by privileging volume over veracity. Automated DFT workflows, accelerated by GPU clusters, flood databases with equilibrium properties but underrepresent kinetic barriers or environmental perturbations, skewing ML training toward idealized scenarios [4, 29]. Autonomous systems, incorporating Bayesian optimization for parameter sweeps, mitigate this through active learning but still grapple with the "exploration-exploitation" dialectic, where predictive fidelity crowds out exploratory probes into regime shifts [13, 30]. Closed-loop experimentation, coupling robotic synthesis with real-time ML feedback, promises remediation yet encounters scalability hurdles in handling epistemic risks—such as model drift under unseen conditions—that erode trust in downstream decisions [14, 31].

Epistemic constraints extend beyond technical artifacts to infrastructural logics. Data-driven materials AI operates within ecosystems where provenance tracking is uneven, multimodal fusion is ad hoc, and feedback loops are predominantly unidirectional [2, 7]. This engenders a conceptual divide: on one side, property prediction as a scalable, modular engine for hypothesis generation; on the other, mechanistic insight as an integrative pursuit demanding traceable causal chains. The former drives throughput but risks epistemic brittleness; the latter fosters depth but curtails breadth [8, 32]. Resolving this divide necessitates a reframing of AI not as an isolated accelerator but as an epistemic scaffold, where computational workflows dynamically negotiate between predictive compression and interpretive expansion.

Key epistemic, infrastructural, and inferential contrasts between property prediction and mechanistic insight paradigms in materials AI are synthesized in Table 1.

This manuscript positions the Interpretive Cascade Framework as a conceptual architecture for navigating this divide. By conceptualizing materials AI as a cascaded system of representational priming, inferential amplification, and steering modulation, the framework illuminates how data infrastructures can engender discovery logics that harmonize efficiency with elucidative power. Drawing on the theoretical underpinnings of representation learning and UQ, it delineates layered interactions that enable feedback-mediated refinement without presupposing empirical validation. In the sections that follow, we synthesize the extant literature to contextualize these dynamics, before articulating the framework's structural contours and systemic implications for computational materials engineering.

Materials data infrastructures

The foundational layer of data-driven materials engineering resides in infrastructures that aggregate and curate multimodal datasets, transforming raw simulation outputs into queryable knowledge bases [2, 6]. High-throughput platforms, such as those leveraging DFT for exhaustive enumeration of binary alloys or ab initio phonon calculations for thermoelectrics, have proliferated, yielding repositories exceeding millions of entries [4, 5]. These ecosystems, exemplified by MaterialsAtlas.org, integrate structural, energetic, and spectroscopic data through standardized ontologies, facilitating seamless ingestion into ML pipelines [18]. Yet, the heterogeneity of sources—spanning experimental X-ray diffraction patterns to computational electron density maps—poses challenges in harmonization, often requiring bespoke preprocessing to align scales and modalities [10, 28].

In this context, multimodal datasets emerge as critical enablers, fusing disparate signals to enrich representational spaces [6, 29]. For instance, coupling solid-state NMR spectra with DFT-derived chemical shifts allows for latent space exploration in crystalline polymorphs, where traditional tabular formats fall short [6]. Such infrastructures underscore a shift from siloed computations to networked data flows, where provenance metadata ensures traceability amid iterative querying [7]. However, the volume-velocity trade-off inherent to these systems—generating data faster than curation can refine it—amplifies epistemic risks, as unvetted entries propagate biases into downstream models [1, 8].

Representation learning architectures

Representation learning stands at the intersection of data infrastructures and inferential machinery, crafting embeddings that distill high-fidelity proxies of materials states [15, 23]. Graph-based architectures, particularly GNNs, have dominated this domain by modeling atomic environments as message-passing networks, capturing radial distribution functions and coordination polyhedra with sub-angstrom precision [11, 12]. Extensions like atomistic line graph neural networks refine this by incorporating bond-angle motifs, enhancing predictions for anisotropic properties in covalent solids [8, 25]. These methods, rooted in equivariant convolutions, preserve symmetry operations, thereby aligning learned features with physical invariances [24, 31].

Parallel advancements in kernel-based representations, such as bispectrum coefficients or persistent homology fingerprints, offer complementary granularity, encoding topological invariants for defect-laden microstructures [11, 32]. In organic-inorganic hybrids, variational autoencoders compress molecular graphs into low-dimensional manifolds, enabling generative sampling for inverse tasks [3, 16]. Collectively, these architectures illuminate a representational continuum: from local descriptors optimizing for predictive fidelity to global embeddings fostering cross-scale analogies [15, 22]. Nonetheless, the interpretive deficit persists, as latent spaces prioritize compressibility over disentanglement, obscuring how features correlate with underlying mechanisms like charge transfer or phonon scattering [23, 27].

AI-guided discovery systems

AI-guided discovery systems operationalize representations through iterative workflows, where ML agents steer exploration in chemical and processing spaces [13, 14]. Bayesian optimization, integrated with surrogate models, has become a staple for parameter tuning in additive manufacturing, balancing acquisition functions to probe uncertainty frontiers [5, 30]. In autonomous laboratories, closed-loop protocols deploy GNNs to forecast synthesis outcomes, feeding experimental feedback into model updates for real-time adaptation [19, 21]. This closed-loop ethos extends to simulation-experiment coupling, where ML-accelerated molecular dynamics informs robotic deposition, compressing discovery cycles from months to days [10, 14].

Such systems embody a probabilistic logic, with ensemble methods aggregating predictions to approximate posterior distributions over property landscapes [17, 26]. Reinforcement learning variants further this by rewarding trajectory efficiency, as seen in navigating phase diagrams for high-entropy alloys [21, 22]. The resultant pipelines—encompassing active learning loops and transfer learning across domains—amplify throughput, yet they reveal fault lines in handling regime discontinuities, such as pressure-induced phase transitions [4, 29]. Here, the interplay between representation and guidance underscores a systemic dependency: robust discovery hinges on embeddings that not only predict but also signal inferential boundaries [12, 20].

Computational design paradigms

Inverse design paradigms invert the predictive arrow, parameterizing target properties to generate viable compositions [2, 10, 15]. Invertible neural networks, for example, map desired bandgaps back to lattice parameters in two-dimensional materials, circumventing exhaustive enumeration [10]. Coupled with genetic algorithms or diffusion models, these approaches populate design spaces for applications like CO2 sorbents, where multi-objective optimization reconciles conflicting metrics [12, 16]. High-throughput screening underpins this, with ML classifiers winnowing candidates prior to DFT validation [3, 4].

The paradigm's strength lies in its modularity: compositional subspaces can be tiled via transfer learning, adapting models from bulk to nanoscale regimes [9, 24]. However, epistemic constraints arise in overparameterized spaces, where generative outputs lack causal anchoring, risking infeasible syntheses [3, 28]. This highlights a broader tension in computational design: the pursuit of optimality often eclipses plausibility, necessitating hybrid logics that interleave forward simulation with backward reasoning [25, 26].

Uncertainty & interpretability

Uncertainty quantification (UQ) and interpretability form the epistemic guardrails of materials AI, quantifying confidence in predictions and illuminating decision pathways [17, 26, 27]. Dropout-based ensembles and conformal prediction provide scalable UQ for regression tasks, flagging aleatoric noise in property extrapolations [6, 26]. Calibration techniques, such as post-hoc bootstrapping, refine these estimates, ensuring that epistemic uncertainty—stemming from data sparsity—guides active querying [27].

Interpretability efforts, meanwhile, leverage attention mechanisms in GNNs to highlight influential subgraphs, as in elucidating grain boundary energetics [31, 32]. Explainable AI (XAI) frameworks dissect model attributions, revealing how elemental features drive toxicity forecasts in perovskites [19, 20]. Yet, these tools operate reactively, dissecting post-training rather than co-designing with representational choices [18, 23]. The resultant insights, while illuminating, struggle with multi-scale causality, where microscale fluctuations cascade into macroscale failures [5, 30]. This synthesis reveals UQ and interpretability not as orthogonal add-ons but as integral to steering logics, where quantified doubts reshape discovery trajectories [7, 21].

Proposed conceptual framework

The Interpretive Cascade Framework conceptualizes materials AI as a dynamic, layered architecture that mediates the divide between property prediction and mechanistic insight through orchestrated flows of representation, inference, and modulation. Unlike modular pipelines that segregate tasks, this framework posits a cascading structure where each layer amplifies the epistemic content of the preceding one, fostering emergent interpretability via recursive interactions. At its core, the framework delineates three structural strata: the representational priming layer, the inferential amplification layer, and the steering modulation layer. These strata interconnect via bidirectional feedback channels, enabling the system to evolve from data ingestion toward hypothesis refinement in a manner that privileges neither prediction nor insight but their symbiotic negotiation.

The representational priming layer initiates the cascade by forging embeddings that encode not merely static features but relational affordances across scales. Drawing from multimodal infrastructures, this layer assimilates structural graphs, spectral signatures, and thermodynamic traces into a unified latent manifold, where nodes represent atomic motifs and edges denote probabilistic transitions [2, 6]. Unlike conventional featurization, which compresses for efficiency, priming emphasizes disentangled hierarchies—separating compositional invariants from environmental contingencies—to prime inference for causal probing [15, 23]. This priming dynamic can be conceptualized as a transformation operator P \mathcal{P} P, where the representational state R \mathbf{R} R emerges from input data D \mathbf{D} D via R=P(D;Θp) \mathbf{R} = \mathcal{P}(\mathbf{D}; \Theta_p) R=P(D;Θp​), with Θp \Theta_p Θp​ denoting tunable priors that balance fidelity and sparsity. Here, P \mathcal{P} P captures the interaction between data heterogeneity and embedding coherence, ensuring that mechanistic signals, such as defect-mediated diffusion paths, persist amid predictive smoothing [11, 24].

Cascading upward, the inferential amplification layer leverages these primed representations to generate layered hypotheses, amplifying predictive outputs into interpretive narratives. Inference here operates as a probabilistic expander, propagating uncertainties through graph convolutions to delineate causal webs—e.g., linking phonon modes to thermal transport anomalies [12, 25]. This amplification mitigates the forward-bias of traditional ML by incorporating contrastive losses that contrast predicted properties against counterfactual perturbations, thereby surfacing relational insights [17, 19]. The layer's logic may be expressed as an amplification functional A \mathcal{A} A, yielding inferred states I=A(R;Φa) \mathbf{I} = \mathcal{A}(\mathbf{R}; \Phi_a) I=A(R;Φa​), where Φa \Phi_a Φa​ encapsulates inference parameters attuned to epistemic depth. This formulation underscores a key trade-off: amplification depth trades against computational breadth, as deeper causal chains enrich insight but narrow the explorable space [20, 32]. In discovery pipelines, this manifests as a data-to-model conduit that evolves from property surrogates to mechanism sketches, informing inverse queries with traceable rationales [10, 16].

The steering modulation layer crowns the cascade, modulating trajectories based on amplified inferences to close feedback loops in autonomous workflows. Steering integrates UQ as an active signal, directing high-throughput queries toward epistemic frontiers—e.g., sampling rare events in phase-field evolutions [4, 5]. Bidirectional channels from lower layers inject modulation signals downward, refining representations on-the-fly to adapt to emergent data streams [13, 14]. This layer's dynamics capture the framework's recursive essence, where steering S \mathcal{S} S refines the cascade via T=S(I;Ψs,F) \mathbf{T} = \mathcal{S}(\mathbf{I}; \Psi_s, \mathbf{F}) T=S(I;Ψs​,F), with Ψs \Psi_s Ψs​ as steering hyperparameters and F \mathbf{F} F denoting feedback aggregates. The interaction term highlights how modulation resolves trade-offs between exploration (insight-seeking) and exploitation (prediction-refining), engendering resilient pipelines that self-correct amid data drift [26, 27].

These strata interweave in data → model → discovery pipelines, where priming feeds amplification, which in turn conditions steering, with feedbacks recirculating refinements. For closed-loop experimentation, this yields workflows that steer robotic synthesis toward mechanistically informed compositions, such as optimizing grain boundaries for fracture resistance [21, 31]. The framework's originality lies in its cascade metaphor: unlike linear taxonomies, it models epistemic flow as a turbulent stream, where eddies of feedback engender interpretive vortices without deterministic closure [7, 18]. The layered interactions and feedback dynamics constituting this epistemic transition are schematically illustrated in Figure 1.

The Interpretive Cascade Framework conceptualizes materials AI as a vertically integrated epistemic architecture in which representational priming, inferential amplification, and steering modulation interact through recursive feedback loops to reconcile predictive efficiency with mechanistic interpretability.

Analytical implications

The Interpretive Cascade Framework yields a suite of analytical implications that reorient the epistemic architecture of materials AI, emphasizing systemic interdependencies over isolated optimizations. By cascading representational, inferential, and steering elements, the framework surfaces trade-offs inherent to discovery infrastructures, where predictive scalability intersects with interpretive resilience. These implications unfold across representational dynamics, inferential workflows, and infrastructural integrations, each illuminating pathways to mitigate the property prediction-mechanistic insight divide.

Representational dynamics and epistemic priming

At the representational stratum, the framework implies a reconfiguration of embedding strategies toward affordance-rich manifolds, where latent structures encode not only predictive proxies but also transitional potentials between material states. This priming logic engenders a representational elasticity, allowing embeddings to adapt via feedback-modulated priors, thereby reducing epistemic lock-in from static featurizations [23, 24]. Analytically, such dynamics foster cross-modal analogies—e.g., aligning spectral embeddings with topological graphs to infer defect hierarchies—without presupposing domain-specific kernels [11, 15]. The resultant implication is a diminished reliance on data volume for robustness; instead, interpretive depth emerges from relational sparsity, where fewer, more disentangled features suffice for extrapolative fidelity [2, 6].

This elasticity can be captured in a conceptual trade-off expression for representational coherence, expressed as  where C denotes coherence, F(R) measures feature fidelity in the primed state S(R) quantifies structural sparsity, and α,β weight the balance between expressiveness and parsimony. This formulation highlights how over-fidelitous representations (α≫β \alpha \gg \beta α≫β) amplify noise in sparse regimes, while sparse priming (β≫α \beta \gg \alpha β≫α) preserves mechanistic signals, steering toward hybrid descriptors that underpin scalable inverse design [10, 16].

Inferential workflows and causal amplification

Inferential amplification within the cascade implies a workflow paradigm where hypothesis generation transcends forward mappings, incorporating counterfactual branching to trace causal lineages across scales. This amplification engenders epistemic branching points, where uncertainties bifurcate into exploratory forks, enabling the framework to delineate regime boundaries—such as entropic transitions in multicomponent systems—prior to resource allocation [17, 20]. The implication extends to discovery pipelines, where amplified inferences serve as navigational beacons, prioritizing mechanistic hotspots over uniform screening [12, 19].

Such workflows reveal a dynamic in uncertainty propagation, conceptualized as , integrating amplification A over representational variations R modulated by inferential perturbations δ(I) This integral captures the propagation of epistemic variance, implying that calibrated amplification—via contrastive objectives—compresses Up ​ in high-confidence loci while expanding it in mechanistic voids, thereby optimizing the inference layer for targeted elucidation [25, 26]. In practice, this steers closed-loop systems toward self-refining loops, where inferential outputs recalibrate representational inputs, fostering emergent insights in multimodal fusion [6, 29].

Infrastructural integrations and steering resilience

Steering modulation implies an infrastructural resilience that embeds UQ as a core governance mechanism, transforming quantified doubts into adaptive directives for high-throughput orchestration [4, 5]. Across autonomous ecosystems, this yields hybrid logics where predictive engines interface with interpretive oracles, mitigating drift in simulation-experiment couplings by recirculating modulation signals [13, 14]. The broader implication is a reimagined data ecosystem, where provenance graphs evolve in tandem with cascade flows, ensuring traceability from atomic priming to mesoscale steering [7, 18].

This resilience manifests in feedback-mediated trade-offs, where steering efficacy hinges on the interplay of exploration depth and exploitation breadth. By positioning the cascade as a modular scaffold, the framework facilitates plug-and-play integrations—e.g., grafting GNN amplifiers onto legacy DFT pipelines—without epistemic rupture [31, 32]. Ultimately, these integrations underscore a systemic insight: materials AI thrives not through monolithic acceleration but via cascaded equilibria, where representational priming sustains inferential vitality, and steering modulation safeguards against epistemic erosion in expansive chemical landscapes [1, 8].

The Interpretive Cascade Framework, as articulated herein, intervenes in the conceptual architecture of computational materials engineering by reframing the property prediction-mechanistic insight divide as a navigable epistemic continuum rather than an intractable binary. This reframing draws salience from the layered cascade, where representational priming sets the stage for inferential depth, and steering modulation ensures adaptive closure, collectively addressing the infrastructural inertias that have long shadowed data-driven discovery [1, 2]. In juxtaposing this framework against extant paradigms, a nuanced interplay emerges: while high-throughput screening excels in breadth, the cascade's recursive logics infuse it with interpretive longitude, enabling workflows that probe not just "what works" but "why it endures" across scales [3, 4].

Central to this discussion is the framework's capacity to illuminate hidden dialectics within AI ecosystems. For instance, the tension between representational compression and inferential expansion—evident in the sparsity-fidelity trade-off—mirrors broader debates in materials informatics, where kernel methods yield precise local approximations yet falter in global analogies [23, 24]. The cascade resolves this by positing amplification as a mediator, where probabilistic expanders disentangle correlated features, akin to how attention mechanisms in GNNs spotlight relational motifs without exhaustive enumeration [11, 12]. This mediation extends to uncertainty handling, transforming UQ from a diagnostic afterthought into a prescriptive force, as seen in calibrated bootstraps that redirect active learning toward causal frontiers [26, 27]. Such dynamics align with the ethos of autonomous systems, where feedback loops—once prone to myopic convergence—now embody a modulated exploration that honors mechanistic contingencies [13, 14].

Yet, the framework's interpretive lens also surfaces prospective contours for infrastructural evolution. In multimodal datasets, the cascade advocates for braided ingestion pipelines that weave spectroscopic and structural strands, mitigating fragmentation that plagues current repositories [6, 28]. This braiding implies a shift toward knowledge graphs with dynamic edges, where inferential outputs retroactively enrich provenance metadata, fostering ecosystems resilient to data obsolescence [7, 18]. For inverse design, the implications ripple into generative steering, where amplification-derived counterfactuals populate feasible subspaces, circumventing the plausibility pitfalls of unanchored diffusion models [10, 15]. Here, the cascade's originality shines: it eschews prescriptive algorithms for systemic grammars, offering a blueprint for hybridizing predictive accelerators with elucidative scaffolds in ways that sustain long-term epistemic vitality [16, 22].

Challenges persist, however, in operationalizing this cascade amid computational realities. The recursive feedbacks, while theoretically elegant, demand lightweight implementations to avoid latency in real-time experimentation [21, 30]. Moreover, the framework's reliance on disentangled representations presupposes advances in scalable autoencoders, underscoring a need for co-design between representational theorists and infrastructure architects [15, 25]. Nonetheless, these frictions are generative, inviting extensions such as multi-agent steering for distributed discovery or contrastive priming for cross-domain transfer [9, 32]. In essence, the cascade positions materials AI at an inflection: from siloed predictions to orchestrated insights, where the divide yields not to dissolution but to dialectical synthesis, propelling computational engineering toward paradigms of enduring ingenuity.

This manuscript has delineated the Interpretive Cascade Framework as a conceptual architecture poised to reconcile the epistemic schism between property prediction and mechanistic insight in materials AI. Through its stratified cascade—priming representations for relational depth, amplifying inferences for causal breadth, and modulating steering for resilient closure—the framework reconfigures discovery pipelines as adaptive epistemes, where data flows engender not mere outputs but traceable narratives of material becoming. Grounded in the theoretical edifice of representation learning and UQ, it navigates the infrastructural trade-offs of high-throughput ecosystems, advocating for workflows that interlace predictive efficiency with interpretive fidelity.

The implications cascade outward, reshaping materials informatics from volume-centric repositories to relational scaffolds that honor multimodal interdependencies. In AI-guided systems, this engenders logics of modulated exploration, where uncertainties steer toward mechanistic horizons, fortifying closed-loop experimentation against epistemic drift. For computational design, the framework illuminates inverse pathways enriched by counterfactual branching, bridging the gap between generative promise and causal grounding. Ultimately, the Interpretive Cascade beckons a maturation of data-driven materials engineering: one where AI serves as epistemic steward, harmonizing the combinatorial vastness of chemical spaces with the intimate logics of atomic agency. By embracing this cascade, the field stands to cultivate discovery infrastructures that not only accelerate innovation but also illuminate its underlying truths, ensuring a legacy of computationally enlightened materials science.

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Ahmed El-Kholy, Nour Abdelrahman & Karim Hassan contributed to this work.

Authors and affiliations

Department of Computational Materials Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt
Ahmed El-Kholy & Nour Abdelrahman

Department of Materials Data Analytics, Faculty of Engineering, Ain Shams University, Cairo, Egypt
Karim Hassan

Corresponding author

Correspondence to Ahmed El-Kholy

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