Predicting the band gap of lead-free inorganic double perovskites using modified parallel residual network
Bibliographic record
Abstract
The rapid and precise screening of appropriate inorganic perovskite materials poses a formidable challenge in the field of materials science. Traditional methods for material screening are not only time-consuming but also demand a substantial workforce. In this study, we introduce a modified parallel residual network (PRN) for the purpose of predicting the band gap of lead-free inorganic double perovskite materials, utilizing the atomic composition as the input data. The predictive performance of PRN is assessed using root mean square error (RMSE) and Pearson correlation coefficient ( r) as evaluation metrics. The PRN model yields a band gap prediction with an RMSE of 0.402 eV and an r value of 0.962, respectively. Notably, PRN outperforms various alternative models, including random forest regression (RFR), kernel ridge regression (KRR), support vector regression (SVR), extreme gradient boosting regression (XGBR), one-layer residual variant network (OLRN), and three-layer parallel residual variant network (TLPRN), clearly demonstrating its superior predictive accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".