Aggregate-State Effects in the Atomistic Modeling of Organic Materials for Electrochemical Energy Conversion and Storage Devices: A Perspective
Bibliographic record
Abstract
Development of new functional materials for novel energy conversion and storage technologies is often assisted by ab initio modeling. Specifically, for organic materials, such as electron and hole transport materials for perovskite solar cells, LED (light emitting diodes) emitters for organic LEDs (OLEDs), and active electrode materials for organic batteries, such modeling is often done at the molecular level. Modeling of aggregate-state effects is onerous, as packing may not be known or large simulation cells may be required for amorphous materials. Yet aggregate-state effects are essential to estimate charge transport rates, and they may also have substantial effects on redox potentials (voltages) and optical properties. This paper summarizes recent studies by the author’s group of aggregation effects on the electronic properties of organic materials used in optoelectronic devices and in organic batteries. We show that in some cases it is possible to understand the mechanism and predict specific performance characteristics based on simple molecular models, while in other cases the inclusion of effects of aggregation is essential. For example, it is possible to understand the mechanism and predict the overall shape of the voltage-capacity curve for insertion-type organic battery materials, but not the absolute voltage. On the other hand, oligomeric models of p-type organic electrode materials can allow for relatively reliable estimates of voltages. Inclusion of aggregate state modeling is critically important for estimating charge transport rates in materials and interfaces used in optoelectronic devices or when intermolecular charge transfer bands are important. We highlight the use of the semi-empirical DFTB (density functional tight binding) method to simplify such calculations.
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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 | 0.001 |
| 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".