The use of machine learning, density functional theory, and molecular dynamics simulations for the designing and screening of efficient small molecule donors for organic solar cells

Abstract

Indeed, a proper understanding of materials is necessary to get the full benefit from them. For this purpose, multiscale computational modeling is the ultimate need. For machine learning analysis, data is collected from the literature. Machine learning analysis is performed using molecular descriptors as independent parameters and power conversion efficiency (PCE) as dependent property. Various machine learning models are tried. The support vector machine (SVM) model has outperformed others. New donor materials that are small molecules are designed using both well‐known and new building blocks. Their PCE is predicted using a SVM model. The top 10 small molecule donors are further studied using density functional theory calculations. Their electronic behavior is studied. Reorganization energy, exciton binding energy and transfer integral are also calculated. Finally, the best three small molecule donors are selected for molecular dynamics simulations. Molecular packing and mixing of active layer materials is studied using radial distribution function. Our proposed framework has the ability to design potential donor materials in short time with marginal computational cost.

An Ortho‐Bisalkyloxylated Benzene‐Based Fully Non‐fused Electron Acceptor for Efficient Organic Photovoltaic Cells

“…The higher intensity of RDF indicates closer molecular packing, which can lead to less phase separation. [28] The RDF intensity of PB2: TBT-6 blend is less than that of PB2: TBT-2, suggesting that that PB2: TBT-6 blend has larger aggregation size than that of PB2: TBT-2 blend, which is in agreement with the result obtained from the AFM results.…”

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

“…Additionally, the combination of DFT calculations with ML has also demonstrated promising results in enhancing the prediction accuracy of ML models, thereby facilitating the screening process. Katubi et al 122 proposed a multiscale computational modeling framework that combines ML algorithms with DFT and MD simulations for designing materials for organic solar cells. Specifically, the SVM algorithm was first constructed using experimental data from the literature, which was then utilized to predict the molecular structure of 10 possible configurations with the highest conversion efficiency.…”

DPP-bridged narrow band gap oligomer-like donor materials: significant effect of molecular structure regulation on photovoltaic performance

“…6 However, as the active layer material of thin film devices, the film-forming property of small organic molecules is usually not as good as that of polymer materials. 7 Therefore, it is a challenge to balance the absorption range, band gap, crystallinity, and phase separation of the active layer material. The development of oligomers with intermediate molecular weights has facilitated the integration of the polymer and small molecule advantages, leading to significant advancements in the photoelectric conversion efficiency 8 b – d .…”