Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach
Bibliographic record
Abstract
Singlet exciton fission is a mechanism that could potentially enable solar cells to surpass the Shockley-Queisser efficiency limit by converting single high-energy photons into two lower-energy triplet excitons with minimal thermalization loss. The ability to make use of singlet exciton fission to enhance solar cell efficiencies has been limited, however, by the sparsity of singlet fission materials with triplet energies above the bandgaps of common semiconductors such as Si and GaAs. Here, we employ a high-throughput virtual screening procedure to discover new organic singlet exciton fission candidate materials with high-energy (>1.4 eV) triplet excitons. After exploring a search space of 4482 molecules and screening them using time-dependent density functional theory, we identify 88 novel singlet exciton fission candidate materials based on anthracene derivatives. Subsequent purification and characterization of several of these candidates yield two new singlet exciton fission materials: 9,10-dicyanoanthracene (DCA) and 9,10-dichlorooctafluoroanthracene (DCOFA), with triplet energies of 1.54 eV and 1.51 eV, respectively. These materials are readily available and low-cost, making them interesting candidates for exothermic singlet exciton fission sensitization of solar cells. However, formation of triplet excitons in DCA and DCOFA is found to occur via hot singlet exciton fission with excitation energies above ∼3.64 eV, and prominent excimer formation in the solid state will need to be overcome in order to make DCA and DCOFA viable candidates for use in a practical device.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".