Organic Photovoltaic Efficiency Predictor: Data-Driven Predictions of Power Conversion Efficiencies of Non-Fullerene Acceptor Organic Solar Cells

Greenstein, Brianna; Hutchison, Geoffrey

doi:10.26434/chemrxiv-2021-nvcx9

Cited by 3 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OPEP/sTD-DFT PCE 54 model was selected as the fitness function due to the high accuracy and low computational cost of the sTD-DFT method. This model has six terms, four of which are from the donor and two from the acceptor.…”

Section: B Fitness Functionmentioning

confidence: 99%

“…Since the goal of this work is to evaluate potential NFAs, the Organic Photovoltaic Efficiency Predictor using simplified time-dependent density functional tions to optimize non-fullerene acceptors composed of electron-accepting (acceptor) and electron-donating (donor) units. theory (OPEP/sTD-DFT) model proposed in our earlier work 54 is used as the fitness function to quickly evaluate the PCE of NFAs. This model requires the difference in energy between the HOMO and HOMO-1 and the sum of the oscillator strengths of the acceptor, obtained through sTD-DFT calculations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells

Greenstein

Hiener

Hutchison

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute-force approaches to screen candidates in a given subset of chemical space, or generative machine learning methods which often require significant training sets. While these methods may find high- performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the "virtual synthesis" of molecules and a prediction of their “fitness” for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empir- ical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron withdrawing/donating strength, as well as the sequence and symmetry of those units are examined. The utilization of a GA over a brute force approach resulted in speedups up to 1.8 × 1012. New types of units not frequently seen in OSCs are suggested, and in total 5,426 NFAs are discovered with the GA. Of these, 1,087 NFAs are predicted to have a PCE greater than 18%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and ac- ceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A′-D-A architecture.

show abstract

Section: B Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells

Greenstein

Hiener

Hutchison

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Greenstein

Hiener

Hutchison

2022

The Journal of Chemical Physics

View full text Add to dashboard Cite

Materials optimization for organic solar cells (OSCs) is a highly active field, with many approaches using empirical experimental synthesis, computational brute-force approaches to screen candidates in a given subset of chemical space, or generative machine learning methods which often require significant training sets. While these methods may find high-performing materials, they can be inefficient and time-consuming. Genetic algorithms (GAs) are an alternative approach, allowing for the "virtual synthesis" of molecules and a prediction of their ``fitness' for some property, with new candidates suggested based on good characteristics of previously generated molecules. In this work, a GA is used to discover high-performing unfused non-fullerene acceptors (NFAs) based on an empirical prediction of power conversion efficiency (PCE) and provides design rules for future work. The electron withdrawing/donating strength, as well as the sequence and symmetry of those units are examined. The utilization of a GA over a brute force approach resulted in speedups up to $1.8 \times 10^{12}$. New types of units not frequently seen in OSCs are suggested, and in total 5,426 NFAs are discovered with the GA. Of these, 1,087 NFAs are predicted to have a PCE greater than 18\%, which is roughly the current record efficiency. While the symmetry of the sequence showed no correlation with PCE, analysis of the sequence arrangement revealed that higher performance can be achieved with a donor core and acceptor end groups. Future NFA designs should consider this strategy as an alternative to the current A-D-A$'$-D-A architecture.

show abstract

Design and implementation of an integrated sun tracking and autocleaning system for photovoltaic panels

Djilali,

Said,

Mahmoud

2024

SEES

View full text Add to dashboard Cite

This paper explores the current research landscape and engineering challenges associated with photovoltaic (PV) systems, with a focus on two critical technologies: sun tracking and self-cleaning mechanisms. Solar panels often suffer from reduced efficiency due to suboptimal orientation and dust accumulation, both of which impede optimal energy generation. To address these issues, an integrated solution is proposed that combines sun tracking for enhanced solar absorption and self-cleaning technologies to maintain panel efficiency. The paper provides a detailed framework for the design and implementation of this dual-purpose system, including hardware components, control algorithms, and energy optimization strategies. Additionally, the proposed system’s performance is evaluated through data analysis, highlighting the potential for increased power output and reduced maintenance costs. This research aims to contribute to the development of more efficient and sustainable solar energy practices, promoting wider adoption of photovoltaic technology in diverse environments.

show abstract

Organic Photovoltaic Efficiency Predictor: Data-Driven Predictions of Power Conversion Efficiencies of Non-Fullerene Acceptor Organic Solar Cells

Cited by 3 publications

References 24 publications

Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells

Computational Evolution of High-Performing Unfused Non-Fullerene Acceptors for Organic Solar Cells

Computational evolution of high-performing unfused non-fullerene acceptors for organic solar cells

Design and implementation of an integrated sun tracking and autocleaning system for photovoltaic panels

Contact Info

Product

Resources

About