TY - JOUR
T1 - Topology without Physics: Structural Abstraction Limits in Graph-Based Materials Models
AU - Sanjay Kulkarni
AU - Meenal Joshi
AU - Rohan Patil
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2022
VL - 1
IS - 2
SP - 92
N2 - The advent of computational and data-driven approaches in materials engineering has transformed discovery pipelines, leveraging machine learning and graph-based representations to navigate vast chemical spaces. However, these models often prioritize topological abstractions over intrinsic physical mechanisms, leading to epistemic constraints in predictive accuracy and interpretability. This manuscript introduces a conceptual framework that dissects the structural abstraction limits inherent in graph-based materials models, emphasizing the trade-offs between computational efficiency and physical fidelity. By synthesizing insights from materials informatics and representation learning, we explore how graph neural networks decouple topological features from underlying physics, potentially hindering autonomous discovery systems and inverse design workflows. The framework delineates layers of abstraction, from data ingestion to inference, highlighting feedback loops that amplify abstraction-induced uncertainties. Implications extend to high-throughput computation, multimodal datasets, and uncertainty quantification, advocating for integrated infrastructures that balance abstraction with mechanistic reintegration. This analysis fosters a deeper understanding of computational steering in materials AI, guiding future developments toward more robust, physics-aware discovery paradigms without empirical validation. Ultimately, addressing these limits could enhance the reliability of data-driven materials engineering ecosystems.
UR - https://iamrp.net/w946411282
ER -