TY - JOUR
T1 - Latent Space Collapse in Materials Representation Learning: A Systems-Level Conceptual Analysis
AU - Hiroshi Nakamura
AU - Yuta Kato
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
DO - 10.68159/l032575922
SP - 88
N2 - In the evolving landscape of computational materials engineering, data-driven approaches have transformed traditional discovery paradigms by integrating machine learning with high-throughput simulations and experimental workflows. Representation learning, particularly through graph neural networks and deep architectures, enables the encoding of complex material structures into latent spaces that facilitate property prediction, inverse design, and autonomous exploration. However, latent space collapse—where embeddings fail to preserve structural diversity or physicochemical distinctions—poses a systemic challenge, undermining the reliability of inference in materials informatics ecosystems. This conceptual analysis frames latent space collapse as an emergent property of interconnected data infrastructures, model architectures, and discovery pipelines, drawing on systems-level interactions across multimodal datasets and uncertainty quantification mechanisms. We introduce the Representation Integrity Framework (RIF), a novel interpretive structure that dissects collapse dynamics through layers of data encoding, model compression, and feedback-driven steering. By examining computational trade-offs and epistemic risks, RIF highlights pathways for resilient representation learning, such as enhanced multimodal integration and adaptive uncertainty handling. Implications extend to closed-loop systems, where mitigating collapse could optimize simulation-experiment coupling and accelerate inverse materials design. This framework advances a balanced view of AI in materials science, emphasizing infrastructure resilience over isolated algorithmic fixes, and informs future developments in foundation models for scientific discovery.
UR - https://iamrp.net/l032575922
ER -