Target bandgap

Desired electronic bandgap for semiconductor candidates.

Neighborhood — ranked by edge-count

method

Self-Correcting Search
about
Technique using internal model representations as feedback loops to steer diffusion-based materials generation toward target properties.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Bandgap-Targeted Generationconcept0.828
Specific materials discovery task: generating semiconductor candidates with desired electronic bandgap using conditioned diffusion.
Target patterns PTconcept0.722
The latent pattern cluster representing the intended task solution.
Viability criteria (stable, unique, novel within target bandgap)concept0.718
Filtering conditions used to assess generated materials candidates.
Anatomical Targetconcept0.705
Target morphologyconcept0.700
The ideal anatomical pattern (e.g., smiling face) that cells aim to achieve.
Bootstrap Applicationconcept0.696
Self-correcting search yields ~+30% improvement in viable candidates within target bandgap range.finding0.695
Main empirical result: interpretability-driven feedback increases discovery efficiency significantly.
Target vs. Off-Target Probe Area Metricmethod0.693
Metric introduced to quantify steering selectivity by comparing the area of target and off-target concept probes.