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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.

In the evolving landscape of computational and data-driven materials engineering, deep neural networks have emerged as powerful tools for accelerating materials discovery and design. These architectures leverage vast multimodal datasets, high-throughput computations, and representation learning to model complex structure-property relationships in materials systems. However, a fundamental tension arises: as network complexity increases to capture intricate physical phenomena, interpretability diminishes, hindering the extraction of scientific insights essential for advancing materials informatics. This interpretability-complexity paradox poses a significant barrier to integrating deep models into autonomous discovery pipelines, where uncertainty quantification and simulation-experiment coupling demand transparent decision-making. To address this gap, we introduce the Interpretive Complexity Equilibrium Framework (ICEFrame), a novel conceptual structure that conceptualizes the dynamic interplay between model depth, representational fidelity, and epistemic transparency in deep materials networks. ICEFrame delineates layered interactions across data ingestion, architectural scaling, and inference steering, incorporating feedback mechanisms to balance trade-offs without empirical validation. This framework offers interpretive lenses for navigating complexity in graph neural networks and foundation models for science, fostering more robust closed-loop experimentation and inverse design strategies. By reframing the paradox through systems-level insights, ICEFrame implications extend to enhancing discovery steering logics in materials AI, ultimately promoting sustainable innovation in computational materials ecosystems.