Experimental efficiency

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed chemistry

35% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 10 + 5 + 5 + 10 + 5 = 35/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: A logged optimization campaign and instrument feedback support bounded automated experimentation. Full campaign economics and autonomous choice of research directions remain gaps. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Run matched optimization campaigns; count failed experiments, consumables, analytics, operator time and elapsed days until the same specification is reached. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Number of experiments, elapsed days, human hours, and total dollars to meet a frozen yield and purity specification.

Evidence requirements

Archive experimental protocols, analytical data, unsuccessful trials, instrument logs, and human interventions. Require independent execution of claimed new routes. Evaluate chemical identity and mass balance rather than accepting plausible descriptions. Compare with both expert chemists and established optimization methods under equal experimental budgets.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Affordable optimization platform; full campaign metric incomplete. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

2026 publication: In photocatalytic isotope-labeling case study 3, optimization used seven initial experiments followed by 30 optimization iterations. RoboChem-Flex, Nature Synthesis.

Interpretation and tracking value

This gives a 37-experiment campaign baseline (project sum), not all-in dollars or time to an independently reproduced specification. Hardware affordability alone is not campaign efficiency.

Next comparable measurement

Run matched optimization campaigns; count failed experiments, consumables, analytics, operator time and elapsed days until the same specification is reached. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.