Computational Modeling of Boron Subphthalocyanines and Subnaphthalocyanines to Justify Their Development and to Predict and Confirm Their Electrochemical and Physical Properties
Bibliographic record
Abstract
For some time, our group has been focused on the molecular design, synthesis and application of derivatives of boron subphthalocyanines (BsubPcs) and subnaphthalocyanines (BsubNcs), which are macrocyclic molecules with a chelated central boron atom. Our focal point has been and continues to be equally balanced between the basic and applied chemistry of BsubPcs/BsubNcs and their application as light absorbing and electronic conducting materials in organic photovoltaics (OPVs)/organic solar cells. Electrochemical properties being critical to this application. For OPVs, we selected a preferred approach to the development of BsubPcs/BsubNcs whereby their molecular design and their synthesis is justified through a development cycle which includes data accumulation of their basic physical chemistry properties, their immediate integration into OPVs and their stability evaluation when applied into OPVs/organic solar cells. Based on data acquisition, we then cycle back to consider alternative molecular designs of BsubPcs/BsubNcs. Recently we have re-Integrated into this cycle our computational modeling methodology which is used to screen potential BsubPcs/BsubNcs for their application in OPVs/organic solar cells and other organic electronic devices. For this presentation I will begin by outlining how we have re-adopted our past computational model to help develop these materials. I will start by showing how we calibrate several levels of computational modeling relative to firm experimental data. I will highlight how a low level and high level computational model can be calibrated and the difference between them. I will then move onto several examples of how we have developed BsubPcs/BsubNcs for application in OPVs and other organic electronic devices utilizing this method. An example I will show is that we have recently identified a pathway to BsubPcs whereby all carbons are bio-sourced. In order to justify their synthesis with the desired OPV application, I will highlight how the computational model justified the time and resource commitment to their synthesis and development. I will also show how the computational calibration model did accurately predict their relevant properties, the prediction being a level of justification for their development. I will outline several other BsubPc/BsubNc macrocycle structures that where either justified by the computational model to be developed or where not justified. I will also highlight to the community progress in avoiding bay-position halogenation of the BsubNc macrocycles during their formation. Additional co-authors/investigators will be identified during this presentation. Figure 1
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".