Predicting Power Conversion Efficiency of Organic Photovoltaics: Models and Data Analysis

Prediction of power conversion efficiency parameter of inverted organic solar cells using artificial intelligence techniques

Organic photovoltaic (OPV) cells are at the forefront of sustainable energy generation due to their lightness, flexibility, and low production costs. These characteristics make OPVs a promising solution for achieving sustainable development goals. However, predicting their lifetime remains challenging task due to complex interactions between internal factors such as material degradation, interface stability, and morphological changes, and external factors like environmental conditions, mechanical stress, and encapsulation quality. In this study, we propose a machine learning-based technique to predict the degradation over time of OPVs. Specifically, we employ multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks to predict the power conversion efficiency (PCE) of inverted organic solar cells (iOSCs) made from the blend PTB7-Th:PC70BM, with PFN as the electron transport layer (ETL), fabricated under an N2 environment. We evaluate the performance of the proposed technique using several statistical metrics, including mean squared error (MSE), root mean squared error (rMSE), relative squared error (RSE), relative absolute error (RAE), and the correlation coefficient (R). The results demonstrate the high accuracy of our proposed technique, evidenced by the minimal error between predicted and experimentally measured PCE values: 0.0325 for RSE, 0.0729 for RAE, 0.2223 for rMSE, and 0.0541 for MSE using the LSTM model. These findings highlight the potential of proposed models in accurately predicting the performance of OPVs, thus contributing to the advancement of sustainable energy technologies.

Interpretable AI and Machine Learning Classification for Identifying High-Efficiency Donor-Acceptor Pairs in Organic Solar Cells

To enhance the efficiency of organic solar cells, accurately predicting the efficiency of new pairs of donor and acceptor materials is crucial. Presently, most machine learning studies rely on regression models, which often struggle to establish clear rules for distinguishing between high- and low-performing donor-acceptor pairs. This study proposes a novel approach by integrating interpretable AI, specifically using Shapely values, with four supervised machine learning classification models, namely, support vector machines, decision trees, random forest, and gradient boosting. These models aim to identify high-efficiency donor-acceptor pairs based solely on chemical structures and to extract important features that establish general design principles for distinguishing between high- and low-efficiency pairs. For validation purposes, an unsupervised machine learning algorithm utilizing loading vectors obtained from the principal component analysis is employed to identify crucial features associated with high-efficiency donor-acceptor pairs. Interestingly, the features identified by the supervised machine learning approach were found to be a subset of those identified by the unsupervised method. Noteworthy features include the van der Waals surface area, partial equalization of orbital electronegativity, Moreau-Broto autocorrelation, and molecular substructures. Leveraging these features, a backward-working model can be developed, facilitating exploration across a wide array of materials used in organic solar cells. This innovative approach will help navigate the vast chemical compound space of donor and acceptor materials essential in creating high-efficiency organic solar cells.

Creation of a structured solar cell material dataset and performance prediction using large language models

Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in applied disciplines. This study introduces a new natural language processing (NLP) task called structured information inference (SII) to address this problem. We propose an end-to-end approach to summarize and organize the multi-layered device-level information from the literature into structured data. After comparing different methods, we fine-tuned LLaMA with an F1 score of 87.14% to update an existing perovskite solar cell dataset with articles published since its release, allowing its direct use in subsequent data analysis. Using structured information, we developed regression tasks to predict the electrical performance of solar cells. Our results demonstrate comparable performance to traditional machine-learning methods without feature selection and highlight the potential of large language models for scientific knowledge acquisition and material development.

Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells

Perovskite solar cells offer great potential for smart energy applications due to their flexibility and solution processability. However, the use of solution-based techniques has resulted in significant variations in device fabrication, leading to inconsistent results on the same composition. Machine learning (ML) and data science offer a potential solution to these challenges by enabling the automated design of perovskite solar cells. In this study, we leveraged machine learning tools to predict the band gap of hybrid organic-inorganic perovskites (HOIPs) and the power conversion efficiency of their solar cell devices. By analyzing 42 000 experimental datasets, we developed ML models for perovskite device design through a two-step predicting method, enabling the automation of perovskite materials development and device optimization. Additionally, band gap dependence of device parameters from experimental data is also validated, as predicted by the Shockley-Queisser model. This work has the potential to streamline the development of perovskite solar cells (PSCs) and optimize their performance without relying on time-consuming trial-and-error approaches.

Enhancement of Power Conversion Efficiency of Non-Fullerene Organic Solar Cells Using Green Synthesized Au–Ag Nanoparticles

Organic-based photovoltaics are excellent candidates for renewable energy alternatives to fossil fuels due to their low weight, low manufacturing cost, and, in recent years, high efficiency, which is now above 18%. However, one cannot ignore the environmental price of the fabrication procedure due to the usage of toxic solvents and high-energy input equipment. In this work, we report on the enhancement of the power conversion efficiency non-fullerene organic solar cells by incorporating green synthesised Au–Ag nanoparticles, using onion bulb extract, into the hole transport layer poly (3,4-ethylene dioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS) of Poly[4,8-bis(5-(2-ethylhexyl)thiophen-2-yl)benzo[1,2-b;4,5-b′]dithiophene-2,6-diyl-alt-(4-(2-ethylhexyl)-3 fluorothieno[3,4-b]thiophene-)-2-carboxylate-2-6-diyl)]: 3,9-bis(2-methylene-(3-(1,1-dicyanomethylene)-indanone))-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d′]-s-indaceno[1,2-b:5,6-b′]dithiophene (PTB7-Th: ITIC) bulk-heterojunction organic solar cells. Red onion has been reported to contain quercetin, which serves as a capping agent that covers bare metal nanoparticles, thus reducing exciton quenching. We found that the optimized volume ratio of NPs to PEDOT: PSS is 0.06:1. At this ratio, a 24.7% enhancement in power conversion efficiency of the cell is observed, corresponding to a 9.11% power conversion efficiency (PCE). This enhancement is due to an increase in the generated photocurrent and a decrease in the serial resistance and recombination, as extracted from the fitting of the experimental data to a non-ideal single diode solar cell model. It is expected that the same procedure can be applied to other non-fullerene acceptor-based organic solar cells, leading to an even higher efficiency with minimal effect on the environment.

OntoPESScan: An Ontology for Potential Energy Surface Scans

In this work, a new OntoPESScan ontology is developed for the semantic representation of one-dimensional potential energy surface (PES) scans, a central concept in computational chemistry. This ontology is developed in line with knowledge graph principles and The World Avatar (TWA) project. OntoPESScan is linked to other ontologies for chemistry in TWA, including OntoSpecies, which helps uniquely identify species along the PES and access their properties, and OntoCompChem, which allows the association of potential energy surfaces with quantum chemical calculations and the concepts used to derive them. A force-field fitting agent is also developed that makes use of the information in the OntoPESScan ontology to fit force fields to reactive surfaces of interest on the fly by making use of the empirical valence bond methodology. This agent is demonstrated to successfully parametrize two cases, namely, a PES scan on ethanol and a PES scan on a localized π-radical PAH hypothesized to play a role in soot formation during combustion. OntoPESScan is an extension to the capabilities of TWA and, in conjunction with potential further ontological support for molecular dynamics and reactions, will further progress toward an open, continuous, and self-growing knowledge graph for chemistry.