Year: 2022
Type: article
Abstract: The present theoretical analysis has identified and formalized a measurement problem analog within materials informatics.Claim 1 asserts that AI-guided measurement does not passively reveal an observer-independent distribution but actively constructs that distribution through resource depletion, physical sample alteration, and selection feedback.The three derived corollaries-path dependence of measurement sequences, active construction bias in datasets, and the learning of the measurement process itself-follow directly from this claim.At the same time, the three mechanisms supply the concrete pathways through which non-neutrality manifests.By distinguishing these classical effects from the quantum measurement problem along ontological, temporal, and epistemic dimensions, the framework clarifies both the shared epistemic core of non-neutral observation and the materials-specific character of its realization.The implications extend beyond conceptual clarification.Autonomous laboratories, active-learning algorithms, and dataset curation protocols must henceforth treat measurement as intervention rather than passive recording.Only measurementaware AI systems-those that internalize resource costs, track meta-uncertainty over observer-induced changes, and document full measurement provenance-can claim epistemic responsibility for the materials they discover.The measurement problem in materials informatics is therefore not a peripheral inconvenience but a foundational feature of data-driven science in the AI era.Recognizing and modeling this feature promises to deepen our understanding of discovery itself as a co-evolutionary process between observer and observed.
DOI: 10.68159/b745414198
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7211892087