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In the evolving landscape of computational and data-driven materials engineering, multi-task learning systems have emerged as pivotal infrastructures for accelerating discovery pipelines. These systems leverage shared representations across diverse material properties to enhance predictive accuracy and efficiency in high-dimensional spaces. However, a critical yet underexplored aspect is the entanglement of properties within these models, where interdependencies among physical, chemical, and structural attributes create emergent behaviors that influence overall system dynamics. This manuscript introduces a novel conceptual framework, the Property Entanglement Lattice (PEL), which interprets cross-property interactions as lattice-like structures facilitating integrated inference and discovery steering. By synthesizing recent advancements in materials informatics, machine learning architectures, and representation learning, we delineate how entanglement manifests in multimodal datasets and foundation models, impacting uncertainty quantification and simulation-experiment coupling. The framework elucidates computational workflows that harness entanglement for optimized resource allocation in autonomous systems, without relying on empirical validations. Implications extend to inverse design paradigms, where entangled representations enable more robust epistemic navigation in materials ecosystems. This work provides a systems-level lens for researchers to conceptualize trade-offs in multi-task setups, fostering infrastructural innovations in computational materials science. Ultimately, it positions cross-property entanglement as a core logic for advancing data-driven discovery, balancing technical depth with interpretive insights.

In the evolving landscape of computational and data-driven materials engineering, discovery pipelines integrate machine learning, high-throughput computations, and autonomous systems to accelerate the identification of novel materials. These workflows, encompassing materials informatics, representation learning, and inverse design, operate as structured sequences that process vast datasets to infer properties and guide experimentation. However, inherent in their design are epistemic filters—mechanisms that selectively emphasize certain knowledge pathways while excluding others, potentially limiting the breadth of scientific insight. This manuscript addresses this conceptual gap by examining how computational architectures, such as graph neural networks and foundation models, impose exclusions through representation biases, uncertainty handling, and feedback dynamics. We introduce the Epistemic Filtration Framework (EFF), a novel systems-level model that maps data ingestion, model inference, and discovery steering to reveal excluded epistemic domains. By interpreting pipeline interactions, the framework highlights trade-offs in multimodal integration and simulation-experiment coupling, offering insights into enhancing workflow inclusivity. Implications extend to materials research ecosystems, fostering more comprehensive discovery logics without empirical validation. This conceptual analysis underscores the need for reflective infrastructure design in AI-augmented materials science, balancing efficiency with epistemic completeness.