Beyond FAIR Data: Instrument Traces for Active and Autonomous Scientific Experimentation
2026-08-24 • Digital Libraries
Digital Libraries
AI summaryⓘ
The authors explain that as artificial intelligence helps scientific instruments decide what to measure next, it creates a new type of record called the experimental trajectory. They studied data from a microscope archive with many events and found that current data files show some useful details but miss important information like unsaved changes and decision reasons. To fix this, the authors suggest a new system that keeps synchronized records of the sample, the instrument, and the decisions made. This would help scientists repeat experiments, predict when instruments need fixing, and train operators better.
artificial intelligencescientific instrumentsexperimental trajectoryatomic force microscopydata provenancemetadatareproducibilitypredictive maintenancedecision tracesample trace
Authors
Sergei V. Kalinin, Boris N. Slautin, Yu Liu, Charles Cao
Abstract
Artificial intelligence is turning scientific instruments into active systems in which observations can determine what is measured next. We argue that this creates an additional scientific record, the experimental trajectory, complementing sample provenance, acquired data and metadata, and analysis workflows. Instrument Traces should ultimately be synchronized with Sample Traces describing specimen evolution and Decision Traces recording human or algorithmic choices. We reconstruct an Instrument Trace retrospectively from a longitudinal AFM/PFM archive containing 118,000 timestamped events from 2023-2026. Conventional saved files reveal material campaigns, latent probe and calibration states, session-level complexity, experimental decision grammar, and composite tuning actions. They also expose what is missing, including unsaved tuning and failures, explicit sample/probe identities, complete timing, exogenous state, and decision rationale. We therefore propose a prospective trace architecture that records synchronized sample, instrument, and decision histories, enabling reproducible autonomy, predictive maintenance, counterfactual analysis, operator training, and transfer across facilities.