TY - JOUR
T1 - Discovery Pipelines as Epistemic Filters: What Computational Workflows Exclude
AU - Hiroshi Tanaka
AU - Yuki Sato
AU - Kenji Mori
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2023
VL - 2
IS - 1
SP - 98
N2 - 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.
UR - https://iamrp.net/a645078092
ER -