TY - JOUR
T1 - Temporal Governance Gaps: Oversight Latency in Accelerated Materials Discovery Systems
AU - Nguyen Thanh Huy
AU - Pham Quang Minh
AU - Le Thi Bich
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2025
VL - 4
IS - 2
SP - 131
N2 - The integration of computational modelling, machine learning, and robotic automation has fundamentally altered the tempo of materials discovery. High-throughput density functional theory databases, graph neural networks trained on vast materials corpora, and self-driving laboratories now generate and evaluate candidate structures at rates orders of magnitude beyond conventional workflows. These systems excel at navigating combinatorial spaces and proposing materials with targeted properties, yet the very acceleration they enable exposes a structural vulnerability: oversight latency. Oversight here denotes the epistemic processes—validation against physical reality, uncertainty propagation, causal interpretation, and knowledge consolidation—that anchor computational predictions within reliable materials engineering practice. When discovery pipelines advance faster than these processes can respond, temporal governance gaps emerge. Unvalidated or partially validated candidates propagate through downstream design, risking cascading epistemic errors in applications ranging from energy storage to quantum materials. This article synthesizes the literature on accelerated platforms articulate oversight latency as a systemic, rather than incidental, feature of contemporary data-driven ecosystems. We introduce the Temporal Governance Synchronization Framework (TGSF), an original conceptual architecture that reframes discovery pipelines as coupled dynamical systems whose synchronization determines epistemic integrity. TGSF identifies structural layers, feedback topologies, and steering logics that can align discovery velocity with governance capacity without sacrificing throughput. By foregrounding temporal dynamics, the framework offers infrastructure-level guidance for designing next-generation materials acceleration platforms that are both rapid and epistemically robust. Its implications extend to the sustainable scaling of computational materials engineering and the responsible stewardship of autonomous discovery systems.
UR - https://iamrp.net/f785636664
ER -