Energy Level Prediction of Organic Semiconductors for Photodetectors and Mining of a Photovoltaic Database to Search for New Building Units

Abstract

Due to the large versatility in organic semiconductors, selecting a suitable (organic semiconductor) material for photodetectors is a challenging task. Integrating computer science and artificial intelligence with conventional methods in optimization and material synthesis can guide experimental researchers to develop, design, predict and discover high-performance materials for photodetectors. To find high-performance organic semiconductor materials for photodetectors, it is crucial to establish a relationship between photovoltaic properties and chemical structures before performing synthetic procedures in laboratories. Moreover, the fast prediction of energy levels is desirable for designing better organic semiconductor photodetectors. Herein, we first collected large sets of data containing photovoltaic properties of organic semiconductor photodetectors reported in the literature. In addition, molecular descriptors that make it easy and fast to predict the required properties were used to train machine learning models. Power conversion efficiency and energy levels were also predicted. Multiple models were trained using experimental data. The light gradient boosting machine (LGBM) regression model and Hist gradient booting regression model are the best models. The best models were further tuned to achieve better prediction ability. The reliability of our designed approach was further verified by mining the photovoltaic database to search for new building units. The results revealed that good consistency is obtained between experimental outcomes and model predictions, indicating that machine learning is a powerful approach to predict the properties of photodetectors, which can facilitate their rapid development in various fields.

A catalyst-free cross-coupling of isocyanates and triarylboranes for secondary amide synthesis

“…Triarylboranes are a family of organoboron compounds with relatively weak Lewis acidity and have found optoelectronic and biological applications, 13 as well as have emerged as frequent components in frustrated Lewis pair (FLP)-mediated useful reactions in the past years. 14 In contrast, there are very limited examples thus far using triarylboranes as aryl transfer reagents in cross-coupling reactions, 15 in which transition metals are of the essence for implementation of those reactions. We were delighted to observe the formation of the desired amide 3a in 20% yield while using triphenylborane 2a as the phenyl source (entry 4).…”

Advances in organic photovoltaic cells: a comprehensive review of materials, technologies, and performance

“…OPV cells typically have two types of electrodes: the transparent conductive electrode (TCE) and the metallic electrode. 140 The TCE is typically made of materials such as indium tin oxide (ITO) or uorine doped tin oxide (FTO) and is placed on the side of the cell facing the incident light. 141,142 This electrode is transparent so that the light can pass through it and reach the active layer, and it is electrically conductive so that it can collect the electrons produced by the active layer.…”

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

“…In addition to the bandgap, highest-occupied molecular orbital (HOMO) and lowest-unoccupied molecular orbital (LUMO) also provide crucial information about the material's electronic structure and energy level distribution. Saleh et al [27] predicted the energy levels of organic semiconductor materials in photodetectors to find new building blocks by mining a photovoltaic database. ML methods such as light gradient boosting machine (LGBM) and GBR model were used to train models for predicting HOMO and LUMO energy levels and power conversion efficiency.…”

“…For example, support vector machine (SVM), random forest (RF), and other ensemble models have been important tools in electronic structure properties prediction. [26][27][28][29] Deep learning (DL) has been widely used in optoelectronic materials and often necessitates substantial volumes of data. Convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), and graph neural network (GNN) are mainly applied to properties prediction, [30][31][32] structure prediction, [33][34][35] image analysis, [36] and optimization.…”