Algorithmic Simplicity as Scientific Virtue: A Conceptual Tension in Materials AI Design

In the rapidly evolving field of Artificial Intelligence for Materials Science, algorithmic simplicity is frequently championed as an epistemic virtue, with practitioners prioritizing linear models, shallow architectures, and parsimonious descriptors under the assumption that simpler solutions inherently promote scientific insight and reliability. This paper critically examines the assumption that simplicity is always a scientific virtue in materials AI design, arguing instead that an overemphasis on simplicity introduces high epistemic costs by obscuring the multifaceted, nonlinear, and multi-scale nature of materials phenomena. The analysis unfolds through four interconnected critique points: first, the inherent trade-off between simplicity and predictive accuracy in capturing complex interactions; second, the risk of simplicity functioning as an obscurant that produces misleading yet confident representations; third, the problematic conflation of simplicity with interpretability, where the two concepts are treated as synonymous despite their distinct epistemic roles; and fourth, the fundamental mismatch between simplicity-prioritizing approaches and the intrinsic complexity demanded by real materials systems. These critiques reveal substantial consequences of simplicity bias, including missed opportunities for discovery, underestimation of uncertainty, premature model acceptance, and inefficient research pathways. Ultimately, the paper proposes alternative approaches that embrace appropriate complexity—matching model sophistication to problem demands, employing regularized complexity, leveraging ensemble methods, designing structured complex architectures, and adopting complexity-aware evaluation frameworks—thereby advocating for a more nuanced valuation of model complexity in service of genuine scientific understanding in materials discovery.

Materials AI has increasingly embraced algorithmic simplicity as a guiding principle in model design, often favoring linear regression techniques, small decision trees, or low-dimensional embeddings for property prediction tasks. This preference stems from a deeply ingrained belief that simpler models align with core scientific ideals of clarity, generalizability, and trustworthiness. Yet the central question persists: is simpler always better when confronting the intricate realities of materials systems? This paper offers a conceptual critique of the assumption that algorithmic simplicity serves as an unalloyed scientific virtue in materials AI design [1-4].

While simplicity can indeed mitigate overfitting and facilitate human comprehension, materials science frequently involves phenomena governed by high-dimensional interactions, emergent behaviors, and multi-scale hierarchies that resist reduction to parsimonious forms. The seed reference by Hansen [3] articulates precisely this conceptual tension, positioning algorithmic simplicity against the demands of accurate materials representation. Historical appeals to Occam’s razor, as discussed by Jefferys and Berger, suggest that simpler explanations should be preferred when they suffice. Yet, Bayesian perspectives from MacKay highlight how complexity penalties must be weighed against data fit [1, 2]. In materials contexts, however, such principles risk oversimplification when applied uncritically.

The problem manifests across workflows where researchers default to simple descriptors over graph-based or deep representations, or opt for shallow networks instead of architectures capable of learning hierarchical features. Butler et al. survey machine learning applications in molecular and materials science, noting the appeal of straightforward approaches for rapid prototyping, while Schmidt et al. document the proliferation of machine learning in solid-state materials, often with an implicit bias toward parsimony [4, 5]. Chen et al. [6] introduce graph networks as a more expressive framework, yet many subsequent works revert to simpler baselines under the guise of virtue.

This paper argues that the valorization of simplicity in materials AI carries epistemic risks that outweigh its benefits in many scenarios. Materials properties—ranging from electronic band structures to mechanical responses in disordered alloys—often depend on subtle, nonlinear couplings that simple models systematically undervalue. Zunger’s discussion of inverse design underscores the need for sophisticated search strategies in functional materials, implicitly challenging overly reductive modeling [7]. By tracing the historical roots of simplicity as virtue and surveying its manifestations in contemporary materials AI, the subsequent sections build toward a structured critique. The goal is not to reject simplicity outright but to distinguish its legitimate applications from problematic overextensions, ultimately calling for a balanced epistemology that values appropriate complexity where materials demand it [3, 8, 9].

Figure 1 illustrates the conceptual architecture through which simplicity bias in materials AI translates a historically valued epistemic heuristic into recurrent modeling practices, four forms of epistemic distortion, downstream scientific consequences, and complexity-aware corrective alternatives.

Figure 1 shows how simplicity, originally valued as a scientific heuristic, becomes operationalized as recurrent low-complexity modeling practices in materials AI, generating four epistemic distortions: reduced predictive adequacy, obscurant simplification, conflation of simplicity with interpretability, and mismatch with the ontological complexity of materials systems. These distortions produce downstream scientific consequences, including missed discoveries, underestimated uncertainty, premature model acceptance, and wasted research effort. Figure 1 concludes with five complexity-aware alternatives that reposition simplicity as a contextual heuristic within a broader principle of appropriate complexity.

The notion of simplicity as a scientific virtue traces back centuries, crystallized in Occam’s razor, which posits that entities or explanations should not be multiplied beyond necessity. Jefferys and Berger explore this principle through a Bayesian lens, demonstrating how simpler models often receive higher posterior probability when they adequately explain observations [1]. MacKay’s work on Bayesian interpolation further formalizes how complexity is penalized in model selection, promoting parsimony as a safeguard against overfitting [2]. In philosophy of science, parsimony functions as an epistemic heuristic, guiding inference toward explanations that invoke fewer assumptions while maintaining explanatory power.

Legitimate roles for simplicity abound in scientific practice. Simple models prevent overfitting by constraining parameter space, thereby enhancing generalization to unseen data—a point echoed in various machine learning contexts. They also promote interpretability, allowing researchers to trace causal pathways or physical mechanisms more readily. In materials informatics, parsimonious descriptors can distill essential chemical or structural features, facilitating rapid screening and hypothesis generation. Vasudevan et al. [9] emphasize the value of parsimony alongside Bayesianity and causality in moving beyond off-the-shelf deep learning for materials problems.

Yet simplicity’s virtue is contextual rather than absolute. Bayesian model comparison, as MacKay outlines, balances fit and complexity through evidence metrics, but this assumes the true generating process lies within the considered model class [2]. When reality exceeds simple assumptions—as is common in condensed matter systems—parsimony can mislead. The seed paper by Hansen [3] identifies this tension explicitly in materials AI, where algorithmic simplicity collides with the need for expressive representations.

Parsimony in scientific inference also serves aesthetic and pragmatic ends. Elegant equations or low-parameter models evoke a sense of beauty and economy, aligning with broader scientific values. Butler et al. [4] highlight how machine learning for materials benefits from interpretable baselines before scaling to complexity. Schmidt et al. [5] review advances where simpler machine learning variants provide initial insights into solid-state phenomena. However, these benefits hold primarily when the underlying phenomena admit compact descriptions. In cases of emergent complexity, such as phase transitions or defect interactions, enforcing simplicity distorts understanding rather than illuminating it.

Acknowledging these legitimate roles does not preclude critique. Simplicity functions best as a regulative ideal or starting point, not an endpoint. Over time, scientific progress often involves moving from simple approximations to more nuanced models as data and theory accumulate. The history of physics, from Newtonian mechanics to quantum field theory, illustrates this progression. In materials AI, a similar trajectory is observable but frequently arrested by an undue attachment to parsimony. Chen et al.’s graph networks exemplify a shift toward greater expressivity for molecular and crystal systems, challenging the default simplicity bias [6]. Zunger advocates inverse design strategies that inherently require navigating complex search spaces [7].

Thus, while simplicity retains value in preventing gratuitous complexity and fostering generalization, its elevation to an unqualified virtue risks stifling inquiry in domains where complexity is ontologically necessary. The following sections examine how this plays out specifically in materials AI applications [1-3, 8, 10].

Table 1 distinguishes legitimate simplicity from epistemically harmful simplicity by showing that parsimony contributes to scientific understanding only when it remains proportionate to the causal and representational demands of the materials problem.

Within materials AI, algorithmic simplicity manifests in multiple recurring forms, each underpinned by the assumption that parsimonious models yield superior scientific outcomes. Linear models for property prediction remain popular due to their transparency and low computational cost. Researchers often employ simple descriptors—such as elemental compositions or basic geometric features—rather than complex graph or embedding representations. Shallow networks or decision trees are preferred over deep architectures, and low-dimensional latent spaces are favored for visualization and analysis [4, 5, 9].

Butler et al. [4] provide a comprehensive overview of machine learning for molecular and materials science, noting the widespread use of straightforward regression techniques as entry points for property prediction. Schmidt et al. [5] document recent advances, where many studies begin with baseline simple models before considering more elaborate alternatives. Chen et al. [6] propose graph networks as a universal framework, yet their work is often cited alongside simpler baselines that assume lower complexity suffices for many crystals and molecules.

The implicit assumption throughout much of the literature is that simpler equates to better—more robust, more interpretable, and more aligned with physical intuition. Vasudevan et al. [9] call for parsimony in representations while critiquing unbridled deep learning, reinforcing the notion that excessive complexity should be curtailed. Lu et al. apply interpretable machine learning strategies to soft-magnetic properties in metallic glasses, favoring approaches that maintain relative simplicity [10]. Zhong et al. discuss explainable machine learning in materials science, where simpler surrogate models are frequently invoked to approximate complex black-box predictions [8].

Surveying the field reveals a pattern: simple linear or tree-based models for bandgap prediction, formation energy estimation, or stability assessment dominate initial explorations. Oviedo et al. [11] address interpretable and explainable methods for materials and chemistry, often contrasting them with more complex neural approaches. Desai et al. [12] introduce parsimonious neural networks that learn physical laws, blending simplicity with targeted expressivity. Gallegos et al. [13] explore explainable chemical AI, where parsimony aids in deriving insights from molecular properties.

In solid-state contexts, Zhou et al. [14] employ machine learning for phase design in high-entropy alloys, beginning with relatively compact models. Singh et al. [15] augment interpretable models with language techniques, yet retain core simplicity in base structures. Moitzi et al. [16] and Kim et al. [17] present frameworks for multi-principal element alloys and metamaterials that, while advancing capability, still navigate tensions between simple arithmetic operations in latent spaces and underlying complexity.

La Cava et al. [18] and Korolev et al. [19] contribute to symbolic regression and transformer-based predictions, where simplicity in symbolic forms or careful regularization is prized. Bell et al. [20] empirically examine accuracy-explainability trade-offs, underscoring the pull toward simpler models in practical deployments. Dziugaite et al. [21] analyze statistical impacts of enforcing interpretability through simplicity constraints.

This prevalence of simplicity in materials AI is not accidental but rooted in the epistemic valorization traced earlier [1-3]. Papers routinely justify model choices by invoking reduced complexity as a virtue, citing improved generalization or easier validation. Yet this survey also hints at underlying strains. When simple models underperform on validation sets involving disordered or multi-component systems, the field often responds by incremental tweaks rather than reconsidering the simplicity imperative. The next sections critique this pattern through targeted lenses [4, 5].

A primary critique of simplicity-prioritizing approaches in materials AI centers on the unavoidable trade-off with predictive accuracy. Simple models, by design, constrain representational capacity, often failing to capture the nonlinear and high-order interactions that govern many material properties. For instance, a linear regression model applied to electronic bandgap prediction may adequately fit simple semiconductors but systematically deviates when confronted with complex band structures influenced by orbital hybridizations, spin-orbit coupling, and lattice distortions [5, 6].

In high-entropy alloys, simple compositional descriptors frequently miss entropic stabilization effects and local chemical ordering that emerge only through intricate many-body interactions. Zhou et al. illustrate machine learning guided exploration of phase design rules, where compact models risk overlooking subtle compositional thresholds [14]. Similarly, for thermal stability in Fe-based metallic glasses, Lu et al. demonstrate that interpretable yet relatively simple strategies must be augmented to handle amorphous complexity adequately [10].

A second materials-specific example arises in interface phenomena, such as grain boundaries or heterostructures. Simple low-dimensional embeddings cannot encode the multi-scale energetics and electronic reconstructions at play. Zunger’s inverse design framework implicitly requires navigating far richer spaces than parsimonious models afford [7]. Chen et al.’s graph networks succeed precisely because they move beyond simplistic pairwise descriptors to capture crystal connectivity more faithfully [6].

Third, in metamaterial design, Kim et al. show that even simple arithmetic in latent spaces benefits from underlying complex representations; purely reductive models fail to generate diverse functional responses [17]. These cases argue that accuracy should, in targeted scenarios, override the default preference for simplicity. While regularization techniques can temper complexity, as noted in broader discussions of model selection [2], materials AI often encounters regimes where the data-generating process demands greater expressive power.

The critique does not advocate unchecked complexity but identifies the epistemic cost of systematically subordinating accuracy to simplicity. When simple models produce lower errors on training data through underfitting rather than genuine insight, they undermine the very scientific progress they purport to support. Butler et al. and Schmidt et al. document workflows where initial simple models serve as benchmarks, yet persistent reliance on them delays adoption of more accurate, albeit complex, alternatives [4, 5]. Vasudevan et al. caution against off-the-shelf approaches lacking sufficient parsimony balanced with needed capacity [9].

In summary, simplicity versus accuracy constitutes a genuine tension rather than a false dichotomy resolvable by defaulting to the former. Materials AI would benefit from explicit acknowledgment that, for certain properties and systems, accepting higher complexity yields superior epistemic returns [3, 8, 11, 20, 22, 23].

Overly simple models in materials AI can function not as clarifiers but as obscurants, generating predictions that appear confident while masking underlying physical realities. A simple descriptor-based model for phase stability might fit available data yet ignore critical vibrational entropy or electronic entropy contributions, leading to overgeneralized rules that fail under varied conditions. This produces a false sense of understanding, where the model “explains” observations through reductive mechanisms that do not correspond to actual causal structures [9, 14].

Consider disordered systems such as amorphous materials or glasses. A parsimonious linear combination of average coordination numbers may correlate with macroscopic properties but obscures local environment heterogeneity responsible for property distributions. Lu et al.’s work on soft-magnetic properties highlights how even interpretable strategies must confront this risk when simplicity truncates relevant variance [10]. Similarly, in high-entropy alloys, simple rule-based models for phase formation can obscure the role of competing electronic and configurational effects, as explored in machine learning appraisals [14].

A further example involves defect dynamics and diffusion in solids. Simple activation energy models based on averaged barriers neglect correlated jumps or trap interactions that dominate transport in real microstructures. Such models yield smooth but misleading predictions, obscuring pathways for materials optimization. Oviedo et al. discuss challenges in achieving genuine interpretability when simplicity hides these nuances in chemistry and materials contexts [11].

Simplicity here acts as an obscurant by promoting surface-level correlations as deep insights. Gallegos et al. [13] note that while explainable AI can derive properties, overly parsimonious forms risk confident but incomplete narratives. Desai et al. [12] advocate parsimonious networks for physical laws, yet warn against extremes that distort learned relationships.

The epistemic harm lies in how such models discourage deeper investigation. Researchers may accept “simple and sufficient” explanations prematurely, halting inquiry into emergent or context-dependent behaviors. Zhong et al. [8] address explainable machine learning where surrogate simplicity can mask the true complexity of underlying processes. This critique distinguishes productive simplification—which distills without loss of essential dynamics—from obscurant simplicity that erases critical information.

In materials AI, where validation against experiment or higher-fidelity simulation is resource-intensive, the allure of simple models amplifies this danger. The field must remain vigilant against simplicity that conceals rather than reveals  [1-3, 16, 17].

A pervasive issue in materials AI lies in the frequent conflation of algorithmic simplicity with interpretability, as if the two were interchangeable or even synonymous. Many practitioners assume that reducing model complexity—through linear regressions, shallow trees, or sparse descriptors—automatically yields greater insight into underlying mechanisms. Zhong et al. highlight the common tradeoff where increased model complexity challenges explainability, yet this does not imply that all simple models are inherently interpretable or that complex ones must remain opaque [8]. The seed conceptual paper by Hansen [3] directly engages this tension, questioning whether prioritizing simplicity truly advances understanding in materials design.

In practice, simple models can obscure interpretability in subtle ways. A linear model with dozens of hand-crafted or high-dimensional descriptors may appear parsimonious in functional form but becomes opaque when tracing how each coefficient contributes to a prediction, especially if features are collinear or derived through opaque preprocessing. Butler et al. [4] survey machine learning applications and note that while baseline simple models aid initial exploration, their interpretability diminishes when features lose direct physical mapping. Conversely, structured complex models—such as graph networks with attention mechanisms or physics-informed architectures—can offer clearer mechanistic insights by explicitly encoding symmetries or hierarchical relations. Chen et al. [6] demonstrate this with graph networks that provide universal yet expressive frameworks for molecules and crystals, allowing interpretation through learned connectivity rather than enforced sparsity.

Three materials-specific examples illustrate the conflation’s pitfalls. First, in predicting properties of Fe-based metallic glasses, Lu et al. employ interpretable strategies that balance simplicity with domain relevance; however, forcing excessive parsimony risks losing insight into local atomic environments that drive magnetic and thermal behaviors, where moderately complex representations better reveal structure-property links [10]. A purely linear descriptor set might yield a compact equation, but it fails to disentangle competing effects like compositional fluctuations and short-range order. Second, for high-entropy alloys, simple rule-based or low-parameter models, as critiqued in phase design explorations by Zhou et al., conflate ease of formulation with true interpretability; emergent configurational entropy and local ordering require models that can articulate multi-element interactions without reductive averaging that hides causal drivers [14]. Third, in metamaterial or inverse design contexts, Zunger emphasizes sophisticated search strategies; here, overly simple latent spaces or embeddings may produce numerically stable outputs but obscure how geometric or electronic degrees of freedom couple, whereas structured complex models can trace pathways to target functionalities more transparently [7].

This conflation carries epistemic costs by discouraging the development of models that achieve interpretability through design rather than reduction. Oviedo et al. discuss how interpretable and explainable techniques for materials science and chemistry must navigate trade-offs among completeness, understandability, and scientific validity, showing that complex models can yield domain-grounded explanations when properly structured [11]. Vasudevan et al. argue for parsimony, Bayesianity, and causality beyond off-the-shelf deep learning. Yet, their call implicitly decouples raw simplicity from genuine insight, advocating representations that are compact yet causally informative [9].

Distinguishing the concepts is essential: simplicity refers to low parameter count or shallow architecture, while interpretability concerns the degree to which a model’s internal logic maps to domain concepts accessible to experts. Legitimate simplicity—such as regularization for generalization—differs from problematic oversimplification that sacrifices mapping fidelity. The field would benefit from explicit decoupling, evaluating models on both dimensions independently rather than assuming one entails the other [1, 2, 20-22]. By maintaining this distinction, materials AI can pursue expressive yet insightful architectures without defaulting to reductive heuristics.

Materials systems are ontologically complex, exhibiting multi-scale hierarchies, emergent phenomena, and nonlinear couplings that simple models fundamentally struggle to accommodate. High-entropy alloys, for example, derive unique properties from configurational disorder, local chemical heterogeneities, and competing electronic interactions across multiple principal elements—features that resist capture by low-dimensional or linear approximations. Moitzi et al. and related works on multi-principal element alloys underscore how ab initio frameworks reveal trade-off relationships only when complexity is explicitly modeled [16]. Enforcing simplicity here risks collapsing rich phase spaces into misleading averages.

A second example concerns interfaces and defects in crystalline or amorphous materials. Grain boundaries, heterointerfaces, and defect clusters involve long-range elastic fields, charge redistributions, and vibrational modes that operate across disparate length and time scales. Simple descriptor-based approaches often average these effects, missing critical emergent behaviors such as localized electronic states or diffusion pathways. Kim et al. show that even latent-space arithmetic for metamaterials benefits from underlying complex representations to generate diverse responses [17]. Third, in soft-magnetic or stability predictions for metallic glasses, Lu et al. demonstrate that thermal and magnetic responses depend on subtle short-to-medium range ordering; parsimonious models may fit global trends but fail to predict outliers or guide optimization where local complexity dominates [10].

Materials science thus demands appropriate complexity rather than its avoidance. Schmidt et al. review advances in solid-state machine learning, where expressive frameworks increasingly supplant simplistic baselines for accurate representation of crystal and molecular systems [5]. Zunger’s inverse design paradigm requires navigating vast, high-dimensional spaces to identify target functionalities, implicitly rejecting the notion that parsimony suffices for discovery [7]. Chen et al.’s graph networks succeed by embracing relational complexity inherent to atomic connectivity [6].

The critique identifies that simplicity bias stems from a mismatch between model assumptions and material reality. While parsimony aids in well-behaved, low-variance regimes, many materials problems involve irreducible complexity arising from quantum-mechanical many-body effects, thermodynamic ensembles, or processing histories. Butler et al. acknowledge that machine learning for materials benefits from scalable representations, yet warn against underpowered models that cannot generalize across chemical spaces [4]. Vasudevan et al. call for balanced parsimony integrated with causality, recognizing that off-the-shelf simplicity often falls short [9].

Distinguishing legitimate simplification (e.g., effective coarse-graining grounded in physics) from problematic reduction (arbitrary truncation of relevant degrees of freedom) is crucial. Materials AI should adopt a complexity-matching principle: model expressivity scaled to the intrinsic demands of the phenomenon rather than an a priori preference for parsimony. This shift would better align algorithmic tools with the epistemic goals of uncovering mechanisms in inherently complex systems [3, 8, 11, 23-29].

The overvaluation of algorithmic simplicity in materials AI produces several interlocking consequences that hinder scientific progress.

Missed discoveries of complex phenomena. By favoring parsimonious models, researchers may overlook subtle emergent behaviors or rare compositional regimes where high-order interactions dominate. For instance, in high-entropy alloys or disordered systems, simple descriptors can miss novel phases or property extrema that graph-based or hierarchical models would surface [14, 16]. This narrows the discovery frontier, channeling effort toward incremental refinements of known simple patterns rather than genuine innovation.

Underestimation of uncertainty. Simple models often produce overconfident predictions by underrepresenting variance from unmodeled factors. In property prediction tasks involving metastable or multi-scale materials, this leads to misleading reliability assessments, as the model appears robust within its constrained space but fails when extrapolated. Zhong et al. and related discussions on explainable methods note how complexity-aware approaches better quantify epistemic uncertainty [8].

Premature model acceptance. Simplicity serves as a proxy for validation, where compact models gain acceptance based on elegance rather than rigorous testing against diverse conditions or higher-fidelity references. Oviedo et al. highlight that interpretability techniques must still ensure scientific validity beyond surface transparency [11]. This risks embedding flawed assumptions into downstream design pipelines.

Wasted research effort. Forcing complex materials problems into simplistic frameworks consumes resources on hyperparameter tuning, feature engineering, or post-hoc explanations that ultimately cannot compensate for representational inadequacy. Vasudevan et al. critique off-the-shelf approaches lacking sufficient capacity balanced with parsimony and causality [9]. Effort diverts from developing tailored, complexity-appropriate methods toward retrofitting ill-suited baselines.

These consequences compound, creating feedback loops that reinforce simplicity bias across the community [1-5]. While simplicity offers short-term pragmatic gains, its systematic prioritization erodes long-term epistemic robustness in materials discovery.

To counter simplicity bias, materials AI should embrace frameworks that value appropriate complexity.

Complexity as needed—match model sophistication explicitly to problem demands through diagnostic assessments of intrinsic material complexity (e.g., via multi-scale analysis or information-theoretic measures) before model selection [6, 7]. Regularized complexity—employ penalties that discourage gratuitous parameters while permitting expressive capacity where data and physics justify it, extending Bayesian approaches like those of MacKay to materials-specific priors [2, 9]. Ensemble complexity—combine multiple simple base learners or modular components whose interactions capture emergent relations without monolithic black-box architectures, leveraging strengths while mitigating individual weaknesses [4, 11].

Structured complexity—design architectures with built-in domain symmetries, hierarchies, or causal graphs (e.g., physics-informed graph networks) that remain interpretable despite higher capacity. Chen et al. and Zhong et al. point toward such pathways [6, 8]. Complexity-aware evaluation—assess models not only on accuracy and parsimony but on metrics capturing mechanistic fidelity, uncertainty calibration, and discovery potential across regimes of varying material complexity [3, 5, 20].

These alternatives promote a nuanced epistemology that distinguishes productive from obstructive simplicity while harnessing complexity where materials ontology requires it. Implementation demands interdisciplinary collaboration between AI practitioners and domain experts to define “appropriate” thresholds contextually.

Table 2 converts the manuscript’s critique into a complexity-aware evaluation framework that reorients model selection from default parsimony toward scientifically adequate expressivity, calibration, and discovery potential.

This critical critique has examined the assumption that algorithmic simplicity constitutes an unqualified scientific virtue in materials AI design. Tracing its historical roots in Occam’s razor and Bayesian parsimony, surveying its prevalence in property prediction and descriptor choices, and dissecting four core problems—accuracy trade-offs, obscurant effects, interpretability conflation, and mismatch with material demands—reveals substantial epistemic costs. Consequences range from missed discoveries to distorted uncertainty quantification and inefficient workflows.

Rather than rejecting simplicity outright, the analysis distinguishes its legitimate regulative roles from overextensions that hinder understanding of complex, multi-scale, and emergent materials phenomena. Alternative approaches that embrace matched, regularized, ensemble, structured, and evaluated complexity offer a path forward.

Materials AI must move beyond default parsimony toward a mature valuation of appropriate complexity in service of deeper insight and more reliable discovery.

None

None

None

None

Siti Rahman, Ahmad Zaki, Nurul Huda, Amir Faisal & Lim Wei contributed to this work.

Authors and affiliations

Department of Intelligent Materials Engineering, University of Malaya, Kuala Lumpur, Malaysia
Siti Rahman, Ahmad Zaki & Amir Faisal

Department of Computational Materials Analytics, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Nurul Huda & Lim Wei

Corresponding author

Correspondence to Siti Rahman

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.