Prediction of optical spectra of coarse-grained polymers as a sequence generation problem: the Recurrent Neural Networks solution
Bibliographic record
Abstract
Coarse-grained simulations of conjugated polymers have become a popular way of investigating the device physics of organic photovoltaics. While UV-Vis spectroscopy remains one of key experimental methods for the interrogation of these devices, a rigorous bridge between coarse-grained simulations and spectroscopy has never been established. Here we address this challenge by developing a method that predicts spectra of conjugated polymers directly from coarse-grained representations while avoiding ad-hoc procedures such as back-mapping from coarse-grained to atomistic representations followed by computing the spectra using standard quantum chemistry methods. Our approach is based on a generative deep learning model: the long-short-term memory recurrent neural network (LSTM-RNN) and it is suggested by the apparent similarity between natural languages and the mathematical structure of the perturbative expansions of the excited state energies due to small fluctuations of the polymer conformation. We use this model to demonstrate a dangerous discrepancy between the spectra obtained in the coarse-grained representation and after the back-mapping. This indicates that standard protocols may require additional fine-tuning in order to become reliable and that our model presents a novel tool uniquely suited for improving the back-mapping protocols and for including spectral data in the development of coarse-grained potentials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".