The exploration of enhanced methods in data science and applications in materials science

Abstract This study develops a structured framework for improving data quality governance and regression reliability in materials informatics (MI). The contributions include: (i) a composite data quality indicator (R : M ratio) for evaluating regression coherence, (ii) an encoder–decoder mechanism for restructuring heterogeneous laboratory data into ML-compatible representations, (iii) a design-space control strategy integrating feature evaluation and clustering, and (iv) a coefficient-informed masking process for model-guided yield optimization. These components are integrated within a conceptual MI platform emphasizing process-level consistency. A laboratory dataset comprising 520 samples and 260 multivariate features was analyzed using multiple machine learning (ML) architectures to evaluate regression performance and design-space behavior. Experimental results indicate that regression coherence, as measured by the R : M ratio, is associated with improved predictive stability, while structured feature and cluster validation enhance design-space interpretability. The masking strategy generates model-derived projections suggesting potential yield improvements under controlled conditions. Beyond empirical evaluation, the study highlights methodological challenges related to data foundation, structural alignment, and model validation. By integrating regression-coherence metrics with structured design-space control, this work advances more transparent and reproducible MI workflows.