Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches
Bibliographic record
Abstract
Abstract This study presents a comprehensive dataset that encompasses the indoor device performance of organic photovoltaic (OPV) materials, their corresponding SMILES codes, and frontier molecular orbital (FMO) energy levels. This dataset comprises a total of 128 subsets and features 64 pairs of donors and acceptors. We demonstrate that traditional models, such as the Shockley–Queisser limit and Scharber’s model, are insufficient for accurately predicting the behavior of indoor OPVs based on the molecular orbitals of these materials. In contrast, we explore the predictive capabilities of four machine learning (ML) models for estimating the power conversion efficiencies (PCEs) of indoor OPVs, utilizing molecular structure information and FMO data from the dataset we compiled. The trained ML models exhibit strong predictive performance with high correlation coefficients ( r > 0.8) for indoor PCE values; notably, the support vector regression (SVR) model achieves the highest r of 0.878. The generalization capabilities of the models are also assessed using previously unseen materials, and the results demonstrate high accuracy rates. The SVR algorithm reaches the best average accuracy of 92.1%, underscoring its potential for efficiently screening materials for indoor applications. Our findings suggest that this dataset, with opportunities for future expansion, could significantly facilitate material design and accelerate computer-aided materials screening, reducing the need for extensive experimental testing in the development of indoor OPVs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".