Multivalent Graph Matching for Symbol Recognition

Ho, D. K.; Ramel, J. Y.; Monmarché, Nicolas

doi:10.1007/978-3-030-86159-9_35

Cited by 1 publication

(1 citation statement)

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“…We constructed ML models using a random forest (RF) algorithm to predict the photovoltaic parameters for BHJ-OPVs under 1 sun AM 1.5 G illumination (see Supporting Information for a detailed description of the RF algorithm). 30 The ML models were trained using 199 datasets consisting of an input encoder and target photovoltaic parameters. Figure 3 shows the predicted values versus the experimental values for the training set.…”

Section: Resultsmentioning

confidence: 99%

Molecular structural descriptor‐assisted machine learning for organic photovoltaics with perylenediimide acceptors

Kim,

Yoon,

Lee

et al. 2023

Bulletin Korean Chem Soc

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Although organic photovoltaics (OPVs) have evolved over the last two decades, the discovery of new materials and optimization of numerous considerations for high‐performance devices remain challenging. To reduce these laborious processes and expedite the advancement of OPVs, we constructed machine learning (ML) models that predict photovoltaic parameters. We designed a unique descriptor that divides the molecular structure into smaller units and translates them into a concise matrix. This allows the ML model to easily track structural units and understand which units are important for predicting target performance, enabling the ML model to prioritize crucial units. Therefore, without requiring additional data from measurements or calculations, the ML models can extract chemical properties from molecular structural information and accurately predict the photovoltaic parameters. The ML models that predict the photovoltaic parameters, including the open‐circuit voltage, short‐circuit current density, fill factor, and power conversion efficiency, all show remarkably superior prediction performance, with Pearson correlation coefficients exceeding 0.68. Consequently, in this article, we propose a highly precise and reliable predictive OPV‐ML platform that can robustly screen for unnecessary experiments and accelerate OPV development.

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Section: Resultsmentioning

confidence: 99%