Accelerated discovery of boron-dipyrromethene sensitizer for solar cells by integrating data mining and first principle
Bibliographic record
Abstract
Boron-dipyrromethene (BODIPY) is one promising class of sensitizers for dye-sensitized solar cells (DSSCs) due to unique merits of high absorption coefficient and versatile structural modification capability. However, such derivatives usually suffer from limited power conversion efficiencies (PCEs) because of narrow light absorption band and low electron injection. To aid the discovery of BODIPY sensitizers, we employ an inverse design method to design efficient sensitizers by integrating data mining and first-principle techniques. We establish robust data-mining models using genetic algorithm and multiple linear regression, where the features are filtered from 5515 descriptors and their meanings are explicitly explored for next inverse designs. Based on the features’ understanding, we design candidates NH1-6 and predict their PCEs, demonstrating remarkable enhancements (58% maximum) compared to previous works. Furthermore, their optoelectronic properties including maximum absorption wavelengths, oscillator strengths, bandgaps, transferred charges, charge transferred distances, TiO2 conduction band shifts, short-circuit currents and electron injection efficiencies simulated via first-principle calculations indicate significant increasements (93 nm, 122.41%, 23.70%, 36.36%, 471.17%, 63.64%, 28.55%, 107.86% maximum), which testifies the corresponding highly predicted PCEs and may overcome BODIPY dyes’ shortcomings. The as-designed BODIPY sensitizers can be promising candidates for DSSCs, and such method could help accelerate the discovery of other energy materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".