# Artificial Intelligence in Organic Photovoltaics: Predicting Power Conversion Efficiency From the Molecular Chemical Structure of (Donor/Acceptor) Pairs

Khoukha Khoussa, Larbi Boubchir, Patrick Lévêque

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Khoukha Khoussa, Larbi Boubchir, Patrick Lévêque. Artificial Intelligence in Organic Photovoltaics: Predicting Power Conversion Efficiency From the Molecular Chemical Structure of (Donor/Acceptor) Pairs. Engineering Reports, 2025, 7 (8), 10.1002/eng2.70334. hal-05236508

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RESEARCH ARTICLE

OPEN ACCESS

# Artificial Intelligence in Organic Photovoltaics: Predicting Power Conversion Efficiency From the Molecular Chemical Structure of (Donor/Acceptor) Pairs

Khoukha Khoussa¹ | Larbi Boubchir² | Patrick Lévêque¹

¹ICube Laboratory, University of Strasbourg-CNRS, Strasbourg, France | ²LIASD Laboratory, University of Paris 8, Saint-Denis, France

**Correspondence:** Patrick Lévêque (patrick.leveque@unistra.fr)

**Received:** 12 March 2025 | **Revised:** 24 July 2025 | **Accepted:** 26 July 2025

**Keywords:** artificial intelligence | artificial learning | feature engineering | molecular chemical structure descriptors | organic photovoltaics | prediction

## ABSTRACT

Organic solar cells (OSCs) can achieve power conversion efficiencies around 20%. Yet, further improvements in efficiency and long-term stability are necessary to rival the dominant silicon technology. Key factors influencing OSC performance include device architecture and the active-layer semiconducting organic materials. In this study, we utilize artificial intelligence (AI) techniques to analyze an experimental dataset of organic semiconductors used in the active layer of OSCs. We propose an AI-based methodology to predict the performance of OSCs using the chemical structure of Donor-Acceptor (D/A) pairs. The method employs Simplified Molecular Input Line Entry System (SMILES) representations to extract molecular features. These features, selected according to maximum relevance and minimum redundancy criteria, are used by supervised machine learning regression algorithms to predict the main photovoltaic parameters. Our AI model demonstrates significant predictive power. Further, we use our model to predict the photovoltaic parameters of (D/A) pairs that were not included in our initial dataset. These findings highlight the potential of AI-driven analysis to accurately estimate the photovoltaic potential of new (D/A) pairs before synthesizing them and therefore to accelerate the development of commercially viable OPV devices and to lower the materials research cost.

## 1 | Introduction

Renewable energy (RE) sources are on a steady rise, offering a sustainable and environmentally friendly alternative to conventional energy (CE) sources. Embracing these alternatives brings numerous advantages for both the environment and society. Firstly, RE aids in combating climate change by slashing greenhouse gas emissions, thus mitigating the adverse effects of global warming like extreme weather events and rising sea levels [1]. Moreover, sources such as solar and wind power are abundant and never-ending, unlike finite CE sources, ensuring long-term energy security and stability. Additionally, investing in renewables drives economic growth and generates employment opportunities across various sectors like manufacturing,

installation, and maintenance of renewable energy infrastructure. Furthermore, utilizing RE reduces reliance on imported CE, bolstering energy independence and resilience to supply disruptions. Overall, transitioning to renewable energy is imperative for constructing a sustainable future, fostering environmental stewardship, and ensuring fair access to clean and affordable energy for all [2, 3].

Solar cells, also known as photovoltaic cells, are integral to this renewable energy process. They convert sunlight into electricity through the photovoltaic effect [4]. Their types vary, including traditional silicon-based inorganic cells and newer organic cells made of polymers or small molecules [1]. Traditional single-junction photovoltaic cells (PVs) predominantly utilize

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© 2025 The Author(s). *Engineering Reports* published by John Wiley & Sons Ltd.
crystalline silicon, either in a single crystal or polycrystalline structure. Renowned for their efficiency and durability, they can, however, incur high production costs, especially during the silicon purification process that requires high temperatures. On the other hand, organic photovoltaics diverge in their production method, employing a thin layer of organic semiconductors deposited at low temperature onto a rigid or flexible substrate, which offers the significant advantage of adaptability to various shapes and sizes [1, 4, 5]. Organic photovoltaics currently exhibit lower power conversion efficiency and shorter lifespans, around 30 years estimated, compared to their traditional inorganic counterpart [6]. Nevertheless, they represent a promising leap towards sustainable energy solutions, offering a range of advantages over inorganic PVs. OPVs with an active layer consisting of semiconducting organic molecules (small molecules or polymers) boast unparalleled flexibility, being lightweight and potentially transparent. This flexibility opens numerous possibilities, enabling integration into various applications such as wearable electronics, portable solar chargers, and Building Integrated Photovoltaics (BIPV) [2, 7]. Moreover, their manufacturing process, utilizing mainly low-temperature solution processing techniques and printing, is not only cost-effective but also environmentally friendly, with significantly lower energy requirements compared to traditional PVs. Furthermore, OPVs exhibit remarkable advancements in efficiency, with recent developments pushing *PCE* slightly below 20% efficiency [1, 7, 8]. While still slightly lower than traditional PVs, (26.1% certified *PCE* for non-concentrated single crystal silicon solar cells) [9] the *PCE* gap is rapidly narrowing, propelled by ongoing research and innovation in the field of organic electronics.

Recent trends in materials science involve integrating material properties with photovoltaic parameters through the utilization of artificial intelligence to develop robust quantitative structure-activity relationships (QSARs) [10]. AI is now present in many fields of science, such as physics, chemistry, biology, healthcare, material science, and many others. It is also increasingly used in all sectors of industry, such as automotive, aerospace, semiconductors, energy, and so forth [11]. In materials science, particularly in OPV technology, AI serves as a powerful tool for generating new molecular structures, allowing researchers to efficiently explore extensive chemical spaces, and this capability not only accelerates the discovery and development of novel compounds with desirable properties but also saves significant time and reduces costs [12]. It can also develop predictive models to understand the relationship between the molecular structure of materials and their performance and many other uses AI to optimize the OPV development [13–16]. Therefore, collaborations between groups working in AI and materials science are essential to unlock the full potential of these technologies. However, many problems can be faced when applying AI to chemical data which is complex to explore [17, 18]. For example, some AI techniques, in particular deep learning, may require large amounts of experimental data [15, 19, 20].

The present paper introduces a novel methodology for predicting the photovoltaic parameters ($V_{oc}$, $J_{sc}$, *PCE*) of organic photovoltaic devices. We consider only bulk heterojunction (BHJ) organic solar cells with binary blends, that is, an electron-donor (D) and an electron-acceptor (A) molecule (see definition below).

We also consider only non-fullerene acceptors (NFAs) in the active layer and not electron-acceptors that are fullerene derivatives. We utilize the D and A chemical structure descriptors in combination with experimental data. Unlike previous studies that often depend on calculated or simulated data, our approach is characterized by the exclusive use of authentic experimental data, including Frontier Molecular Orbitals, specifically the HOMO (Highest Occupied Molecular Orbital) and the LUMO (Lowest Unoccupied Molecular Orbital) to ensure the authenticity and applicability of our findings to practical scenarios. Moreover, unlike other works that frequently include duplicate (D/A) pairs due to their reliance on datasets collected from the literature, where slight variations in performance for the same (D/A) across different laboratories are often ignored, we strictly focused on using unique ones to eliminate redundancy and to mitigate the risk of overfitting, ensuring that our predictive models remain robust and reliable. Additionally, our dataset encompasses a diverse range of high and low performing OPVs, in contrast to many existing studies that predominantly focus on high-performing devices. This diversity enhances our model's ability to predict performance across a broader spectrum of OPVs, making it applicable to both low-performance and high-performance systems. We also incorporate in our work both small molecules and polymers to achieve a level of variety that is rarely addressed in previous research and which allows our methodology to accommodate a wider range of OPV material systems, improving its generalizability and predictive accuracy.

Deep pretreatment process is then used with several machine learning models and a careful tuning of their hyperparameters in order to predict the photovoltaic parameters of OPVs. The proposed methodology yields comparable results to other recent approaches from literature [12, 21]. Furthermore, by employing a minimal number of features to train our models, we achieved accurate prediction results, demonstrating the efficiency of our methodology. Finally, five different experimental recent data, not included in the initial dataset of the present work, were used to highlight in a very conclusive way the predictive capability of our model.

## 2 | Experimental Section

### 2.1 | Organic Photovoltaic Parameters

Organic solar cells are composed of multiple layers with an organic material layer, also known as active layer (AL), positioned between two electrodes as shown in Figure 1. The device structure is most of the time more complex, with charge-carrier selective layers between the AL and the electrodes.

The AL is the part of the OPVs where the process of converting sunlight into charge-carriers occurs. It typically consists of a blend of semiconducting organic materials (an electron-donor (D) one and an electron-acceptor (A) one: Figure 1) that can absorb photons. If a photon is absorbed by the electron-donor in the AL (i.e., if the photon energy is higher than the D band-gap), an exciton (a bond electron-hole pair) is generated. This exciton can then diffuse to an interface between D and A where the electron can be transferred from the LUMO of D to the LUMO of the A while the hole will stay in the HOMO of D, leading to the
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** | Organic photovoltaics device general structure.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** | Current density versus voltage curve under darkness (closed circles) and under standard illumination (open circles) and electrical power under illumination (red curve).</figcaption></figure>

generation of a Charge-carrier Transfer State (CTS). This CTS will potentially lead to the creation of free charge carriers that can move in the AL under the electric field due to the electrode work function difference [5].

Several morphologies exist for the AL. The first efficient one was the bilayer structure with A and D layers on top of each other [22], then Bulk Heterojunction was introduced later to maximize the D/A interface and favor exciton dissociation [23]. Each design aims to enhance device performance, and recently, modified bilayer elaboration was shown to be quite efficient (PCEs over 19%) [24]. In the present work, we focus on the BHJ structure, by far the most employed in the last years. We further analyze data measured on binary blends.

Evaluating the efficiency of OPVs includes measuring different photovoltaic parameters under standard (AM1.5G

(100 mW/cm²)) illumination conditions. The first important photovoltaic parameter is the short-circuit current ($I_{sc}$) which is the current under standard illumination at zero bias voltage. For the sake of comparison between cells of different surfaces, the short-circuit current density ($J_{sc}$) which is $I_{sc}$ divided by the surface of the cell, is often used. The second parameter is the open-circuit voltage ($V_{oc}$) or the voltage for a zero current under illumination. The third one is the Fill Factor (FF) which is the ratio between the maximum electrical power per surface unit and the product of $V_{oc}$ and $J_{sc}$, and quantifies the rectifying behavior of the OPV solar cell, as mentioned in Figure 2.

Finally, the power conversion efficiency (PCE) is the electrical power (by surface unit) divided by the light power (by surface unit). Under standard illumination conditions (AM1.5G (100 mW/cm²)), PCE (in %) is simply equal to the product of $J_{sc}$ (in mA/cm²) by $V_{oc}$ (in V) by FF (no unit) [23]. Together,
these metrics provide valuable insights into the performance of OPVs. In the present paper, we focused on the prediction of $V_{oc}$, $J_{sc}$, PCE, and the fourth photovoltaic parameter (FF) was calculated.

## 2.2 | Related Work

In recent years, the prediction of organic photovoltaics performances has garnered significant attention due to its potential to revolutionize renewable energy technologies [16]. Semi-empirical and theoretical models have traditionally served as the cornerstone for estimating FMOs and ultimately OPV performances. Theoretical models, such as those based on density functional theory (DFT) or other quantum mechanical approaches, allow for calculating the best molecular conformation and the corresponding FMOs in vacuum or in solution in different solvents. Theoretical models are computationally intensive and require domain-specific expertise [25, 26]. Semi-empirical models rely on FMOs of the donor material (D) and the acceptor one (A) and on some experimental hypotheses to quickly estimate, using predictive equations, the photovoltaic parameters of BHJ solar cells [27]. Recently, machine learning (ML) has emerged as a powerful tool to complement traditional approaches [11–13, 21, 28]. Leveraging experimental datasets and advanced algorithms, machine learning can uncover complex patterns and relationships, enhancing predictive accuracy and efficiency in estimating OPV performance.

However, OPVs research poses a significant challenge for ML applications due to the limited experimental data availability. This strong limitation restricts the robustness and generalizability of ML models, hindering their effectiveness in accurately predicting material properties, device performance, and optimizing manufacturing processes [29]. Further, the inherent complexity and variability of organic materials exacerbate this issue, as ML requires large and diverse datasets for comprehensive model training. Consequently, researchers face obstacles in achieving reliable predictions and identifying subtle trends within the data. Addressing this challenge requires innovative approaches such as using transfer learning (TL) and simulated or calculated datasets, such as the Harvard Clean Energy Project (HCEP) dataset [30], which is the largest computational dataset gathered by scientists at Harvard University to help find new materials for clean energy technologies, like solar panels or batteries. It contains data on 2.3 million organic molecules and their properties. This huge dataset was used by Olivares-Amaya, who applied the Random Forest algorithm (RF) to predict solar cell parameters with high correlation scores: PCE (0.84), $V_{oc}$ (0.94), $J_{sc}$ (0.89), and FF (0.61) with the numbers in parentheses being the R² score [31]. Gaussian Process Regressor (GPR) [32] and RF [33] algorithms were also used to predict PCE using the HCEP dataset. However, the prediction results on the validation set were in both cases lower than the one using the HCEP, achieving a coefficient of correlation $r = 0.43$ (49 experimental data) and $r = 0.62$ (350 experimental data).

AI technics have also been used to predict the electronic properties of donor molecules (FMOs). Moore et al. [15] applied a Deep Transfer Learning on the HCEP and on the Harvard Organic photovoltaics dataset (HOPV15) which is a real experimental

dataset collected from the literature [34]. They first trained their model on the HCPE dataset, and then, by using the TL, they retrained the model and obtained a HOMO prediction with an R² of 0.55 and a LUMO prediction with an R² of 0.63. Their study highlights the dataset's significance for advanced predictive models for OPVs. Consequently, emphasizing the importance of experimental data for model training over calculated datasets like the HCEP one, we advocate for the utilization of experimental datasets. Grounded in empirical evidence, these datasets provide more accurate representations of the complexities inherent in real systems, thereby enhancing the reliability and applicability of our models in practical settings [6, 21]. Several studies using experimental data to predict the performance of binary BHJ solar cells, but also tandem solar cells (2 solar cells connected in series) and ternary solar cells (3 different organic semiconductors in the active layer) can be found in the literature. For instance, M. Lee applied random Forest using experimental FMOs of (D/A) pairs to predict PCEs using 70 experimental data for tandem OSCs [35]. The achieved prediction results showed an R² score of 0.69. Afterwards, in a more recent work [36], the dataset was upgraded to 121 ternary datasets, where 26 are polymer donors and 90 are a third component collected from the literature and applied the same model to predict only $V_{oc}$. The generated model showed an R² value of 0.77, a good result considering the small size of the input dataset. Both studies exclusively focused on utilizing FMO of organic semiconductors as descriptors, while overlooking other molecular descriptors and disregarding the potential influence of thin film morphology. The random forest model was also trained on 566 pairs and demonstrated impressive predictive capabilities for power conversion efficiency [28]. However, it is important to highlight that this dataset did not include the Y-series acceptors [12], which are a recent class of materials increasingly incorporated into many high-performing OSCs. Other researchers used the FMO properties calculated with Time-dependent density-functional theory (TD-DFT) and the molecular chemical descriptors like van der Waals surface area descriptors of D and A to predict $V_{oc}$, $J_{sc}$, and the PCE using two of the largest experimental datasets to date collected from the literature [12, 21]. All the aforementioned results are presented in Table 1.

In order to improve the efficiency and the deep understanding of organic photovoltaics, there has been a convergence of experimental and computational approaches. In many studies, data from the HCEP is utilized to construct machine learning models to predict OPVs performance. However, the molecules described in the OPV literature are often significantly more complex than those found in the HCEP dataset. Importantly, our work focuses on unique pairs of D and A molecules (we have excluded duplicates to ensure data integrity) using non-fullerene acceptor (NFA) based organic photovoltaic materials which have shown excellent performances in recent studies. Despite notable advancements in this domain, a comprehensive understanding of the intricate interplay between molecular structure, molecular electronic properties, molecular chemical descriptors, and device performance remains a challenge. Our study endeavors to address this gap by focusing on the predictive potential of experimental data and molecular descriptors using different types of materials for both weak and high performance OPVs.
<table><caption>TABLE 1 | Related works from literature using experimental (Exp) and calculated (Cal) data and our work.</caption><thead><tr><th>References</th><th>Dataset</th><th>Exp or Cal (HCEP)</th><th>Input Type</th><th>Target</th><th>ML models</th><th>Validation</th><th>Validation Metrics</th><th>Results</th></tr></thead><tbody><tr><td>[35]</td><td>70</td><td>Exp</td><td>FMO</td><td>PCE</td><td>RF and SVR</td><td>Train: 70%<br/>Test: 30%</td><td>R<sup>2</sup><br/>RMSE</td><td>R<sup>2</sup>=0.69<br/>RMSE=2.86%</td></tr><tr><td>[36]</td><td>121</td><td>Exp</td><td>FMO</td><td>V<sub>oc</sub></td><td>RF</td><td>Train: 80%<br/>Test: 20%</td><td>R<sup>2</sup></td><td>R<sup>2</sup>=0.77</td></tr><tr><td>[28]</td><td>566</td><td>Exp</td><td>FMO, Weights,<br/>fingerprint<br/>(ECFP6)</td><td>PCE</td><td>RF</td><td>K-Fold CV</td><td>r</td><td>r=0.85</td></tr><tr><td>[31]</td><td>2.3 million</td><td>HCEP</td><td>CSD</td><td>PCE<br/>V<sub>oc</sub><br/>J<sub>sc</sub><br/>FF</td><td>Linear<br/>Regression</td><td>Train test split</td><td>R<sup>2</sup></td><td>R<sup>2</sup>=0.84 (PCE)<br/>R<sup>2</sup>=0.94 (V<sub>oc</sub>)<br/>R<sup>2</sup>=0.89 (J<sub>sc</sub>)<br/>R<sup>2</sup>=0.61 (FF)</td></tr><tr><td>[33]</td><td>51,000</td><td>HCEP +Exp</td><td>Fingerprints</td><td>PCE</td><td>GPR</td><td>Train test split</td><td>r</td><td>r=0.43</td></tr><tr><td>[32]</td><td>2.3 million +<br/>HOPV15</td><td>HCEP, HOPV15</td><td>CSD and<br/>fingerprints<br/>+FMO</td><td>PCE</td><td>SVR, RF,<br/>HDMR</td><td>Train test Split<br/>shuffle +K-Fold<br/>CV</td><td>MSE<br/>MAE<br/>R<sup>2</sup></td><td>MSE=3.18%<br/>MAE=1.41%<br/>R<sup>2</sup>=0.35</td></tr><tr><td>[15]</td><td>2.3 million +<br/>HOPV15</td><td>HCEP + Exp</td><td>2D chemical<br/>structure<br/>images</td><td>FMO of Donor</td><td>CNN</td><td>Train test split</td><td>r</td><td>r=0.623</td></tr><tr><td>[21]</td><td>1242</td><td>Exp</td><td>CSD + FMO</td><td>PCE<br/>V<sub>oc</sub><br/>J<sub>sc</sub></td><td>SVR, RF,<br/>ANN, GBR</td><td>Train: 80%<br/>Test: 20%<br/>+ LOOCV</td><td>RMSE/r</td><td>R<sup>2</sup>=0.55 (HOMO)<br/>R<sup>2</sup>=0.63 (LUMO)</td></tr><tr><td>[12]</td><td>503</td><td>Exp</td><td>CSD + FMO<br/>(TDs-DFT)</td><td>PCE &gt; 10</td><td>RF</td><td>5 Fold CV</td><td>R<sup>2</sup></td><td>R<sup>2</sup>=0.28<br/>RMSE=1.60%</td></tr><tr><td rowspan="2">Our<br/>Work</td><td rowspan="2">924</td><td rowspan="2">Exp</td><td rowspan="2">CSD + FMO<br/>(Exp)</td><td>V<sub>oc</sub></td><td>SVR, RF,<br/>Adaboost,<br/>Xgboost,</td><td>Train: 80%<br/>Test: 20%<br/>+LOOCV</td><td>RMSE<br/>MAE<br/>R<sup>2</sup></td><td>RMSE=0.78 (V<sub>oc</sub>)<br/>R<sup>2</sup>=0.70 (J<sub>sc</sub>)</td></tr><tr><td>J<sub>sc</sub></td><td>PCE</td><td>GBR</td><td>+New data</td><td>r</td><td>R<sup>2</sup>=0.63 (PCE)</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>**FIGURE 3** | Different representations of the same molecule (Y6) to train AI models on the active layer of organic solar cell.</figcaption></figure>

## 2.3 | Dataset

### 2.3.1 | Molecular Representation

In the realm of molecular research, the representation of molecules is pivotal for leveraging the capabilities of AI. From traditional SMILES codes, which encode molecular structures as a text string (more details are provided in Figure S2), to more complex 2D images and graph-based representations, each approach offers unique insights into molecular structure and properties [37]. Additionally, advancements in three-dimensional conformation representation of materials have expanded the repertoire of tools available for molecular analysis and design. In this study, we focus on utilizing SMILES codes presented in Figure 3 as our primary molecular representation as these codes can be easily used with cheminformatics software to calculate a wide range of molecular descriptors (Ex: molecular weights, number of bonds with their types, van der Waals surface area descriptors, etc.), and it can represent complex molecular structures in a compact form. This simplification helps in handling large datasets and reduces computational complexity [37].

### 2.3.2 | Dataset Details

Thousand two hundred and twenty five experimental donor/NFA devices have been collected from various literature sources [12]. This dataset includes crucial parameters such as $V_{oc}$, $J_{sc}$, FF, and PCE; also, when available, $\lambda_{max}$ in solution or film, the optical bandgap, the ionization energy, and the electron affinity. To simplify computational analysis, they standardized all side chains to an ethyl group, thus reducing computational complexity.

To ensure data uniqueness and enhance accuracy, the dataset underwent refinement to include only distinct (D/A) pairs. If

several data points were available for the same (D/A) pair, such as for instance for PM6/Y6 devices, the highest PCE was chosen. Indeed, most of the results published for each pair are already an average over a few cells, but the quantity of data for each (D/A) pair remains too small to make a selection based on a statistical study, as for example in the study by Dang et al. covering 579 articles published between 2002 and 2010 on P3HT/PCBM [38]. Dang et al. study shows the influence of the P3HT characteristics ($M_n$, $M_w$, polydispersity...) but also of the solvents, of the deposition method, of the post-elaboration annealing, and of the solar-cell structure on the PCE for P3HT/PCBM bulk heterojunction solar cells. The PCE was found to be, on average, between 3.5% and 4% with some publications reporting PCE values up to 6%. In the present work, we would have chosen a PCE of 6% for P3HT/PCBM. The aim of this approach is to consider the maximum photovoltaic potential of each pair (D/A) based on the molecular structure and not on molecule batch-to-batch variations or non-optimized OPV device elaboration or structure.

The refined dataset consists of 999 unique (D/A) pairs, comprising 605 distinct acceptors and 214 donors, each accompanied by their respective SMILES codes. This dataset encompasses both small molecules and polymers, adding diversity compared to other datasets used for similar purposes. Exclusion of all pairs that have no reported experimental FMO resulted in a final dataset of 924 containing unique pairs of donor and acceptor with their corresponding SMILES code and electronic properties (FMO).

The dataset contains PCE (%) values between 0.01% and 18.77%, with the most frequent values between 6% and 14% as shown in the histogram represented in Figure 4.
We have also plotted the top 10 molecules used as donors and as acceptors. As shown in Figure 5, the most common donors are PBDB-T (also known as PCE12), PBDB-T-2F (or PBDT-T-F or PM6 in different works), PTB7-Th (or PCE10 or PBDTTT-EFT) and P3HT, and the most common acceptors are ITIC, which is the first non-fullerene acceptor to outperform fullerene acceptors in organic photovoltaics, IT-4F (or ITIC-4F or ITIC-2F), Y6, and IDIC.

## 2.4 | Method

We present here a novel method for predicting the performance of OPV devices using the chemical structures of D/A pairs. The proposed method includes three primary phases: (2.4.1) data pre-processing, (2.4.2) feature extraction and selection, and (2.4.3) prediction process using ML algorithms. Figure 6 depicts an illustrative diagram of the proposed method. The following sections describe all the steps constituting this method.

### 2.4.1 | Data Pre-Processing

In the initial stages of any machine learning process, cleaning and preprocessing the dataset are crucial to ensure compatibility with our ML models. The first task involves removing duplicate entries and considering the commercial names of D and

<figure><img src="image_6.png" /><figcaption>FIGURE 4 | Distribution of PCE (%) values in the dataset.</figcaption></figure>

A molecules. Occasionally, we encounter identical molecules with distinct commercial names like PM6, which is the same molecule as the PBDB-T-F due to various laboratories independently publishing their works simultaneously and naming the same molecule differently. Consequently, it is essential to meticulously filter the dataset to retain only unique (D/A) pairs of molecules. It is also important to check the reported PCE values by recalculating them using the provided $V_{oc}$, $J_{sc}$, and FF. This step is necessary as sometimes the reported PCE values might be inflated or optimized under different experimental conditions, leading to inconsistencies. By recalculating the PCE, we ensure that the reported values are accurate and consistent. This verification helps maintain the integrity and reliability of the dataset for subsequent machine learning analysis.

### 2.4.2 | Feature Extraction

We have extracted more than 100 molecular descriptors using RDKit, which is an open-source toolkit for cheminformatics that provides functionalities to handle chemical informatics tasks such as reading and writing molecules, calculating molecular descriptors, and performing substructure searches, and the SMILES code strings of donor and acceptor as input. For example, we have extracted the molecular weight of D and A, the number of single, double, and triple bonds of D and A, the Estate-VSA (Electrotopological State Volumetric Surface Area) and the number of bicyclic rings, and so forth. After the extraction step, we saved these extracted features in a dataset referred to as “data_1” This dataset contains various RDKit descriptors with the reported performance (PCE, $V_{oc}$, $J_{sc}$, and FF) and the SMILES code of each donor and acceptor pair. Subsequently, we added the reported experimental Frontier Molecular Orbitals along with data_1 and created a new dataset, data_2.

### 2.4.3 | Feature Selection

In our study, we utilized the MRMR (Minimum Redundancy Maximum Relevance) feature selection method to identify the most pertinent chemical descriptors from the extensive dataset generated by RDKit. This approach was instrumental in enhancing the predictive accuracy of our machine learning models.

<figure><img src="image_7.png" /><figcaption>FIGURE 5 | Top 10 common donor and acceptor distribution in our dataset.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**FIGURE 6** | Schematic description of the proposed approach.</figcaption></figure>

MRMR operates by prioritizing features that exhibit strong correlations with the target variables while concurrently minimizing redundancy among selected descriptors [39]. By focusing on these informative yet non-redundant descriptors, we ensured that our models were not only more accurate but also more interpretable, facilitating deeper insights into the underlying chemical factors influencing photovoltaic performance. This methodological choice not only optimized model efficiency but also underscored our commitment to rigorous feature selection, essential for robust predictive modeling in complex chemical systems. The results of the MRMR on the extracted features to predict $V_{oc}$, $J_{sc}$, and PCE can be found in the Supporting Information. We can conclude that the MRMR helps us to use only nonredundant features and with only 20 features out of more than 100 we can achieve good predictive results as mentioned in Figures S3, S4, and S5. A graph that represents the 20 descriptors showing their relevance and redundancy scores in predicting $V_{oc}$ ($V$) is shown in Figure S6.

### 2.4.4 | Prediction Process

The prediction process is realized by employing ML algorithms on data_1 and data_2. Indeed, we have applied five supervised

ML regression models: SVR (Support Vector Regressor), RF (Random Forest), Adaboost Regressor (Adaptive Boosting), XGBoost Regressor (Extreme gradient boosting regressor) and GBR (Gradient Boosting Regressor) on the two distinct datasets data_1 and data_2, while carefully tuning the hyperparameters of each model, which are a set of adjustable parameters that significantly influence model behavior and predictive outcomes. Effective tuning of these hyperparameters through methods like Grid Search enables us to identify the optimal configuration that maximizes predictive accuracy and generalization capability. Also, it can enhance our model's robustness, mitigate overfitting to training data, and improve the model's ability to capture complex relationships within the data.

The proposed methodology is highlighted in Figure 6 as well as Algorithm S1 that summarizes all the steps constituting it.

## 3 | Results and Discussion

### 3.1 | Results Obtained on the Whole Dataset

To assess the performance of our model, we have employed various evaluation metrics such as the coefficient of determination
(R2 score or R²), RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and the correlation coefficient (r). The equations for these metrics can be found in the Supporting Information section.

As shown in Table 2, the best predictions are obtained using GBR and XGBoost. These two models are thought to manage overfitting efficiently compared to SVR and RF. Further, GBR outperforms XGBoost, and this is due to the optimization of the loss function employed in GBR, which is well adapted to the nature and the structure of the data used. For $V_{oc}$ prediction with data_1, using 20% as a test set and 80% as a train test, we obtained notably good results. Specifically, the GBR model achieved an R² value of 0.68, an r of 0.83, and an RMSE of 0.052 V. These metrics indicate a good predictive capability, demonstrating the efficiency of RDKit descriptors in capturing the essential molecular features required for accurate $V_{oc}$ predictions. However, when we expanded the dataset to include experimental FMO data alongside RDKit descriptors (data_2), the prediction performances improved. As mentioned in Table 2, the R² relative increase was almost 15% using experimental FMOs with a score of 0.78 for test and 0.99 for train; the RMSE became as low as 0.045 V for test and 0.01 V for train. The experimental versus predicted values of the $V_{oc}$ are mentioned in Figure 7a. The significant increase in the accuracy of $V_{oc}$ prediction was expected, as it is well known from empirical studies that $V_{oc}$ is directly proportional to the FMOs of the donor and acceptor. Indeed, in the case of weakly absorbing fullerene acceptors, $V_{oc}$ is proportional to the energy difference between the HOMO of the donor and the LUMO of the acceptor [27]. Nevertheless, without FMOs (data_1), the RDKit descriptors alone are also robust, returning reliable predictions for $V_{oc}$. This is crucial, as the FMOs incorporated in data_2 are experimental and therefore need the molecules to be synthesized and experimentally analyzed (using cyclic voltammetry for instance). We could also calculate FMOs in order to avoid molecules synthesis, but calculating FMOs (for instance using DFT and TD-DFT) is also time-consuming and often leads to values substantially different from the measured ones. Therefore, using our model to predict the photovoltaic performances of a given (D/A) pair without synthesizing it and consequently without the experimental FMOs may be more convincing when trained on data_1 instead of trained with data_2. Overall, the findings highlight that while incorporating FMO data can enhance model performance marginally, the use of RDKit descriptors alone is justified by their efficiency and adequacy in producing high-quality predictive models for $V_{oc}$ for a given (D/A) pair. This approach not only simplifies the computational process but also ensures that the predictions remain robust and reliable. Additionally, to rigorously evaluate the model's performance, we also used the Leave-One-Out Cross-Validation (LOOCV). LOOCV is a technique where each data point is used as a validation set while the rest of the data acts as the training set in turn. This process iterates for each data point, ensuring that every sample serves once as a test set (Figure S1).

This method provides a robust assessment of the model's generalization capability, especially beneficial for small datasets. Applying LOOCV to our models trained on datasets featuring RDKit descriptors alone and RDKit descriptors combined with FMO, we observed consistent trends. The models exhibited strong

<table><caption>TABLE 2 | Prediction of OPV performance results by using different ML models applied to data_2.</caption><thead><tr><th rowspan="2">Performance Parameters</th><th colspan="3">V<sub>oc</sub> (V)</th><th colspan="3">J<sub>sc</sub> (mA/cm<sup>2</sup>)</th><th colspan="3">PCE (%)</th></tr><tr><th>R<sup>2</sup></th><th>MAE (V)</th><th>RMSE (V)</th><th>r</th><th>R<sup>2</sup></th><th>MAE (mA/cm<sup>2</sup>)</th><th>RMSE (mA/cm)</th><th>r</th><th>MAE (%)</th><th>RMSE (%)</th></tr></thead><tbody><tr><td>SVR</td><td>0.49</td><td>0.051</td><td>0.068</td><td>0.72</td><td>0.55</td><td>2.72</td><td>3.87</td><td>0.76</td><td>0.54</td><td>1.99</td><td>2.60</td><td>0.75</td></tr><tr><td>RF</td><td>0.63</td><td>0.048</td><td>0.064</td><td>0.82</td><td>0.65</td><td>2.43</td><td>3.40</td><td>0.81</td><td>0.58</td><td>1.90</td><td>2.49</td><td>0.76</td></tr><tr><td>XGBoost</td><td>0.70</td><td>0.040</td><td>0.057</td><td>0.84</td><td>0.67</td><td>2.44</td><td>3.28</td><td>0.82</td><td>0.60</td><td>1.88</td><td>2.43</td><td>0.78</td></tr><tr><td>GBR</td><td>0.99</td><td>0.007</td><td>0.01</td><td>0.99</td><td>0.93</td><td>1.12</td><td>1.59</td><td>0.97</td><td>0.85</td><td>1.12</td><td>1.50</td><td>0.92</td></tr><tr><td></td><td></td><td>(Train)</td><td>(Train)</td><td>and 0.034</td><td>and 0.045</td><td>(Test)</td><td>and 0.70</td><td>(Test)</td><td>and 0.83</td><td>and 0.63</td><td>(Test)</td><td>and 2.37</td><td>and 0.79</td></tr><tr><td></td><td></td><td></td><td>(Test)</td><td></td><td></td><td></td><td>(Test)</td><td></td><td></td><td></td><td>(Test)</td><td></td><td>(Test)</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**FIGURE 7** | Experimental versus predicted values of *V*<sub>oc</sub> (a), *J*<sub>sc</sub> (b) and *PCE* (c) using GBR model with the chemical descriptors of (D/A) pairs (left) and with the chemical descriptors and the FMOs of (D/A) pairs (right).</figcaption></figure>
predictive accuracy across most samples, as indicated by relatively low RMSE values (0.042 and 0.044 V, respectively) for the $V_{oc}$. Also, by leveraging LOOCV, we can effectively mitigate the risk of overfitting, as the model is tested on each individual data point, ensuring that it generalizes well to unseen data [40, 41]. This method provides a reliable estimate of the model predictive capability and ensures that our results are not overly tailored to a specific subset of the data, thus enhancing the overall validity and reliability of our findings.

We employed the same method applied to $V_{oc}$ with $J_{sc}$, while comparing both dataset predictions (with or without experimental FMO) achieving nearly comparable results, showing an $R^2$ value of 0.67, an *r* of 0.82, and an RMSE of 3.33 mA/cm² which indicate a good performance compared to the state of the art using only RDKit descriptors, and an $R^2$ value of 0.70, an *r* of 0.83, and an RMSE of 3.19 mA/cm² when adding the FMO to the input data. This suggests that RDKit descriptors alone also allow an accurate prediction for the $J_{sc}$ parameter. However, some samples, particularly those with $J_{sc}$ values below 2 mA/cm², as shown in Figure 7b, were poorly predicted. This is likely due to their limited distribution in the dataset or to the fact that low-performance solar cells are most of the time less optimized experimentally than the more promising ones. Nonetheless, these samples were included in our analysis to comprehensively explore diverse types of OPV devices, encompassing both high-and low-performing variants. Furthermore, we implemented the same method to predict the *PCE*, which showed comparable results to the state of the art, with an $R^2$ value of 0.61, an *r* of 0.78, and an RMSE of 3.3% using only RDKit descriptors (data_1), and an $R^2$ value of 0.63, an *r* of 0.79, and an RMSE of 2.3% when adding the FMO features (data_2) as shown in Figure 7c.

In order to better understand the links between the molecular descriptors and the photovoltaic parameters, we performed a SHAP (SHapley Additive exPlanations) analysis (Figure S7). As already explained, it is not surprising to find the FMOs of both A and D molecules in the 20 most important parameters for prediction as these frontier energy levels play a major role in photon absorption (energy band-gap of A and D) as well as in the exciton dissociation driving force. $V_{oc}$ is strongly dependent on the FMOs but also on the charge-carrier recombination, which is much harder to anticipate from the main descriptors. The short-circuit current density ($J_{sc}$) is also strongly dependent on the A and D absorption properties (linked to the energy band-gap) but also on the exciton-dissociation efficiency (depending on HOMO and LUMO offsets), on the free charge-carrier

extraction (charge-carrier mobility, active layer morphology, domain purity...). The *FF*, which is not directly predicted in our work, measures the rectifying behavior of the organic solar cell and depends also on the charge-carrier extraction (the cell contacts are not taken into account as the active layer only is considered in our work). Finally, the *PCE* is experimentally calculated from $V_{oc}$, $J_{sc}$, and *FF* and, in our study, it is linked to the HOMO and LUMO of D and A as well as to free charge-carrier generation, transport, and recombination. Free charge-carrier generation, transport, and recombination depend on the active layer morphology (domain size, domain purity, polymer orientation relative to the substrate, percolation paths...) and relating the molecular descriptors to such material bulk properties is quite hazardous. Nevertheless, the D number of aromatic rings (don_NumAromaticRings in Figure S7) may be tentatively linked to the conjugation length of the electron-donor molecule while the A number of single bonds (acc_NumSingleBondss in Figure S7) could be a measure of the balance to be found between the A planarity (linked to the proportion of fused rings) and flexibility in order to avoid macrocrystallization in the A domains. The hydrophobicity of both A and D molecules seems also to be an important property (acc_MolLogP and don_MolLogP in Figure S7). The other parameters are related to the sum of the van der Waals surface area for each atom in a molecule (VSA) and focus on the hydrophobicity (SlogP), the polarizability (SMR) and the electropological character (Estate) [42–44]. These parameters are difficult to interpret as they are related to the D and A molecules and to their interaction. They may be connected to the solubility of the D and A molecules as well as to their mutual interactions and therefore on the morphology of the active layer at a nanoscopic scale.

## 3.2 | Model Validation on Only Experimental PCE > 10%

To compare our best predicting model with the literature, we also used the GBR model on a smaller set of values, including only the experimental *PCE* values higher than 10%. This corresponds to the work of Greenstein [12] (see Table 1) where they used a RF model with a 5-fold Cross Validation on the (D/A) pairs, leading to a *PCE* > 10%. We used the very same validation on our GBR model and obtained a *PCE* prediction with a $R^2$ value of 0.46 (compared to 0.28) and an RMSE of 1.38% (compared to 1.6%). This direct comparison highlights the predictive capability of our GBR model.

<table><caption>**TABLE 3** | Predicting capabilities of our model on unseen experimental (D/A) pairs comparing the predicted (Pred.) and experimental (Exp.) values for the main photovoltaic parameters.</caption><thead><tr><th>Donor</th><th>Acceptor</th><th>Pred.<br>V<sub>oc</sub> (V)</th><th>Exp.<br>V<sub>oc</sub> (V)</th><th>Pred. J<sub>sc</sub><br>(mA/cm<sup>2</sup>)</th><th>Exp. J<sub>sc</sub><br>(mA/cm<sup>2</sup>)</th><th>Pred.<br>PCE (%)</th><th>Exp.<br>PCE (%)</th><th>References</th></tr></thead><tbody><tr><td>D18</td><td>L8-BO</td><td>0.92</td><td>0.92</td><td>25.50</td><td>26.86</td><td>17.44</td><td>19.05</td><td>[45]</td></tr><tr><td>PBDB-TF</td><td>ITIC</td><td>1.00</td><td>1.04</td><td>15.52</td><td>16.00</td><td>9.63</td><td>9.70</td><td>[46]</td></tr><tr><td>D18</td><td>Y6</td><td>0.90</td><td>0.85</td><td>26.80</td><td>27.70</td><td>17.43</td><td>18.22</td><td>[47]</td></tr><tr><td>PBDB-TF</td><td>IT-4F</td><td>0.85</td><td>0.83</td><td>21.39</td><td>20.60</td><td>12.87</td><td>12.88</td><td>[48]</td></tr><tr><td>PBDB-TF</td><td>Y6-1O</td><td>0.89</td><td>0.90</td><td>22.69</td><td>25.51</td><td>16.06</td><td>16.81</td><td>[49]</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>FIGURE 8 | Experimental versus predicted values of V<sub>oc</sub> (a), J<sub>sc</sub> (b), and PCE (c) using GBR model on unseen input data.</figcaption></figure>

3.3 | Model Validation on Unseen (D/A) Pairs

To further evaluate our model, we selected several new exper-
imental results on (D/A) pairs that were not included in our
initial dataset. Our model yielded good results in terms of pre-
dicting V<sub>oc</sub>, J<sub>sc</sub>, and PCE, as shown in Table 3 and Figure 8.
These findings demonstrate that our model performs well with
both high-and poor-performance organic photovoltaics (OPVs)
and can accurately identify them based on the chemical structure
of the (D/A) pairs that constitute the active layer.

Five examples alone are not sufficient to fully establish gen-
eralizability. Nevertheless, their inclusion demonstrates the
model's applicability to real-world systems beyond those it was
trained on. This external validation complements our primary
evaluation methods, which include an 80/20 train/test split
and Leave-One-Out Cross-Validation (LOOCV), both designed
to rigorously assess predictive performance and minimize
overfitting.

4 | Conclusion

This study demonstrates the significant potential of artificial
intelligence in accelerating the development of organic photo-
voltaics, particularly in the identification and optimization of
donor/acceptor materials pairs of the active layer. By utiliz-
ing a Gradient Boosting Regression model in conjunction with
the chemical structure descriptors and the Frontier Molecular
Orbitals of these materials, we have predicted the key perfor-
mance metrics (i.e., V<sub>oc</sub>, J<sub>sc</sub>, and PCE). The RDKit library was
employed to extract chemical structure descriptors from SMILES
representations. These descriptors alone proved to be highly
effective in predicting OPV performances when used as inputs to
the GBR model. The addition of experimental FMO descriptors
offered only a modest improvement. For instance, on the PCE,
the R<sup>2</sup> value increases from 0.61 to 0.63 (relative increase of only
3%) when FMO descriptors are included. Therefore, while FMO
provides additional useful information, the chemical structure
descriptors are robust predictors on their own. To further validate
our model, it was tested against real literature results that were
not included in the initial dataset. This test confirms our model's

accuracy and reliability in predicting OPV performance metrics.
These findings have significant implications. Firstly, they demon-
strate the feasibility of using AI models to expedite the discovery
and optimization of materials in the field of photovoltaics. Sec-
ondly, the methodology established here provides a solid frame-
work for future research aimed at identifying and developing new
D/A pairs, potentially leading to more efficient and cost-effective
OPV materials. Overall, the integration of AI into the research
and development process of OPVs holds great promise for achiev-
ing rapid advancements and innovations in renewable energy
technologies. Future work should focus on expanding the dataset,
refining the models, exploring additional descriptors and molec-
ular features to further enhance predictive accuracy and material
discovery efforts, finding potentially efficient (D/A) pairs, and
exploring the use of a third component in the active layer of effi-
cient OPV cells.

Acknowledgments

K.K. would like to express her sincere gratitude to Dr. Olivier Bardagot for the insightful discussions and valuable input that greatly contributed to the development of this work.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request
from the corresponding author.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Data S1:** Supporting Information.