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Materials informatics represents a transformative intersection of data science, artificial intelligence, and materials engineering, enabling accelerated discovery and optimization of novel substances through computational analysis. However, this paper introduces the concept of epistemic saturation as a critical threshold where accumulating vast datasets no longer enhances meaningful knowledge generation. Instead, it perpetuates interpretive redundancies and systemic distortions, such as entrenched biases in data curation and algorithmic processing. Drawing on recent advancements in machine learning applications within materials science, we explore the dynamics of data-meaning interactions, highlighting how unchecked scaling of information inputs can lead to diminished epistemic value. The proposed framework interprets these phenomena through feedback structures that reveal trade-offs between quantitative abundance and qualitative insight, emphasizing ethical reasoning in algorithmic design and the need for reflexive systems-level oversight. By synthesizing literature on data integrity, algorithmic limitations, and value-laden scientific practices, this conceptual analysis underscores the implications for sustainable innovation across fields such as alloy development and nanotechnology. Ultimately, recognizing epistemic saturation fosters more integrative approaches to informatics, steering toward resilient knowledge ecosystems that prioritize interpretive depth over mere data proliferation. This shift has the potential to reorient materials research toward epistemically robust outcomes amid the ongoing digital transformation.

Neural scaling laws describe the power-law decay of prediction error with increasing training dataset size, yet the scaling exponents reported for materials property prediction vary widely (0.1–0.8) across databases and properties. This variation has remained poorly understood, limiting reliable forecasting of data requirements in computational materials discovery. Here we present a purely theoretical framework that attributes the observed differences in scaling exponents primarily to the intrinsic data redundancy of materials databases. We formalize redundancy as the fraction of samples that convey overlapping or duplicate structural information, thereby reducing the effective number of independent samples. Through conceptual derivations and proof sketches grounded in information theory and sample-complexity arguments, we establish that the effective scaling exponent is approximately the ideal (independent-sample) exponent multiplied by (1 − r), where r is the redundancy fraction. Upper and lower bounds on achievable exponents are derived directly from three fundamental structural features: the number of distinct crystal prototypes, elemental compositional diversity, and the variety of local atomic environments. High redundancy—arising naturally from repeated prototypes, compositional biases, and clustered coordination motifs—systematically flattens the exponent and imposes hard structural limits on learning efficiency, independent of model architecture. The framework provides lightweight, model-free metrics for estimating redundancy from standard database statistics and offers actionable guidance for diversity-driven dataset curation. By reframing scaling behavior in terms of effective sample size and structural diversity, this work supplies a unified theoretical explanation for the wide range of reported exponents and a predictive foundation for designing more efficient materials databases.