Exploration of promising optical and electronic properties of (non-polymer) small donor molecules for organic solar cells

Tuning optoelectronic properties of indandione-based D-A materials by malononitrile group acceptors: A DFT and TD-DFT approach

CONTEXT

Indandione-based materials are promising candidates for organic electronics, offering high electron mobility and tunable optoelectronic properties. In this study, we explore the optoelectronic and photovoltaic properties of indandione-based donor-acceptor (D-A) materials, specifically (R1) and (R2), by introducing malononitrile group acceptors into their molecular structure. These strong electron-withdrawing acceptors are designed to enhance charge transfer and overall material performance. The designed molecules (DM1-DM4) exhibit a low optical band gap of approximately 1.77 eV, significantly lower than the reference materials (R1 and R2) at around 2.24 eV in a solvent environment. Among the designed molecules, DM4 stands out with superior photovoltaic parameters, including a narrow optical band gap (1.77 eV), higher electron affinity (3.49 eV), an extended excited state lifetime (10.0 ns) owing to its low electron and hole reorganization energies (λe ~ 0.13 eV and λh ~ 0.24 eV), and improved short-circuit current density (Jsc) of ~ 15.73 mA/cm2. Notably, DM4 achieves a power conversion efficiency (PCE) of ~ 18.5%, making it an excellent candidate for device applications.

METHOD

A comprehensive computational investigation was carried out on indandione-based D-A materials with malononitrile group acceptors (DM1-DM4) using density functional theory (DFT) and time-dependent DFT (TD-DFT) methods, as implemented in Gaussian 16 software. We examined the electronic and optical properties of the proposed molecules through frontier molecular orbital (FMO) analysis, UV-Vis absorption spectra, density of states (DOS), exciton binding energy (Eb), and transition density matrix (TDM) analysis, utilizing GaussView 6.0 and Multiwfn 3.8 software. The photovoltaic parameters and power conversion efficiency (PCE) were evaluated using the Scharber and Alharbi models.

A Hammett's analysis of the substituent effect in functionalized diketopyrrolopyrrole (DPP) systems: Optoelectronic properties and intramolecular charge transfer effects

Diketopyrrolopyrrole (DPP) systems have promising applications in different organic electronic devices. In this work, we investigated the effect of 20 different substituent groups on the optoelectronic properties of DPP-based derivatives as the donor ( D )-material in an organic photovoltaic (OPV) device. For this purpose, we employed Hammett's theory (HT), which quantifies the electron-donating or -withdrawing properties of a given substituent group. Machine learning (ML)-basedσ m ,σ p ,σ m 0 ,σ p 0 ,σ p + ,σ p - ,σ I , andσ R Hammett's constants previously determined were used. Mono- (DPP-X1 ) and di-functionalized (DPP-X2 ) DPPs, where X is a substituent group, were investigated using density functional theory (DFT), time-dependent DFT (TDDFT), and ab initio methods. Several properties were computed using CAM-B3LYP and the second-order algebraic diagrammatic construction, ADC(2), an ab initio wave function method, including the adiabatic ionization potential ( I P A ), the electron affinity ( E A A ), the HOMO-LUMO gaps (E g ), and the maximum absorption wavelengths (λ max ), the first excited state transition 1 S0 → 1 S1 energies ( ∆ E ) (the optical gap), and exciton binding energies. From the optoelectronic properties and employing typical acceptor systems, the power conversion efficiency ( PCE ), open-circuit voltage (V OC ), and fill factor ( FF ) were predicted for a DPP-based OPV device. These photovoltaic properties were also correlated with the machine learning (ML)-based Hammett's constants. Overall, good correlations between all properties and the different types of σ constants were obtained, except for theσ I constants, which are related to inductive effects. This scenario suggests that resonance is the main factor controlling electron donation and withdrawal effects. We found that substituent groups with large σ values can produce higher photovoltaic efficiencies. It was also found that electron-withdrawing groups (EWGs) reducedE g and ∆ E considerably compared to the unsubstituted DPP-H. Moreover, for every decrease (increase) in the values of a given optoelectronic property of DPP-X1 systems, a more significant decrease (increase) in the same values was observed for the DPP-X2 , thus showing that the addition of the second substituent results in a more extensive influence on all electronic properties. For the exciton binding energies, an unsupervised machine learning algorithm identified groups of substituents characterized by average values (centroids) of Hammett's constants that can drive the search for new DDP-derived materials. Our work presents a promising approach by applying HT on molecular engineering DPP-based molecules and other conjugated molecules for applications on organic optoelectronic devices.

Data mining and library generation to search electron-rich and electron-deficient building blocks for the designing of polymers for photoacoustic imaging

Photoacoustic imaging is a good method for biological imaging, for this purpose, materials with strong near infrared (NIR) absorbance are required. In the present study, machine learning models are used to predict the light absorption behavior of polymers. Molecular descriptors are utilized to train a variety of machine learning models. Building blocks are searched from chemical databases, as well as new building blocks are designed using chemical library enumeration method. The Breaking Retrosynthetically Interesting Chemical Substructures (BRICS) method is employed for the creation of 10,000 novel polymers. These polymers are designed based on the input of searched and selected building blocks. To enhance the process, the optimal machine learning model is utilized to predict the UV/visible absorption maxima of the newly designed polymers. Concurrently, chemical similarity analysis is also performed on the selected polymers, and synthetic accessibility of selected polymers is calculated. In summary, the polymers are all easy to synthesize, increasing their potential for practical applications.

Generation of Chemical Space of Compounds for Prostate Cancer Treatment: Biological Activity Prediction, Clustering, and Visualization of Chemical Space

Designing molecules for pharmaceutical purposes has been a significant focus for several decades. The pursuit of novel drugs is an arduous and financially demanding undertaking. Nevertheless, the integration of computer-assisted frameworks presents a swift avenue for designing and screening drug-like compounds. Within the context of this research, we introduce a comprehensive approach for the design and screening of compounds tailored to the treatment of prostate cancer. To forecast the biological activity of these compounds, we employed machine learning (ML) models. Additionally, an automated process involving the deconstruction and reconstruction of molecular building blocks leads to the generation of novel compounds. Subsequently, the ML models were utilized to predict the biological activity of the designed compounds, and the t-SNE method was employed to visualize the chemical space covered by the novel compounds. A meticulous selection process identified the most promising compounds, and their potential for synthesis was assessed, offering valuable guidance to experimental chemists in their investigative endeavors. Furthermore, fingerprint and heatmap analysis were conducted to evaluate the chemical similarity among the selected compounds. This multifaceted approach, encompassing predictive modeling, compound generation, visualization, and similarity assessment, underscores our commitment to refining the process of identifying potential candidates for further exploration in prostate cancer treatment.

Effect of tailoring π-linkers with extended conjugation on the SJ-IC molecule for achieving high VOC and improved charge mobility towards enhanced photovoltaic applications

The problem of low efficiency of organic solar cells can be solved by improving the charge mobility and open circuit voltage of these cells. The current research aims to present the role of π-linkers, having extended conjugation, between the donor and acceptor moieties of indacenodithiophene core-based A-π-D-π-A type SJ-IC molecule to improve the photovoltaic performance of pre-existing SJ-IC. Several crucial photovoltaic parameters of SJ-IC and seven newly proposed molecules were studied using density functional theory. Surprisingly, this theoretical framework manifested that the tailoring of SJ-IC by replacing its π-linker with linkers having extended π-conjugation gives a redshift in maximum absorption coefficient in the range of 731.69-1112.86 nm in a solvent. In addition, newly designed molecules exhibited significantly narrower bandgaps (ranging from 1.33 eV to 1.93 eV) than SJ-IC having a bandgap of 2.01 eV. Similarly, newly designed molecules show significantly less excitation energy in gaseous and solvent phases than SJ-IC. Furthermore, the reorganization energies of DL1-DL7 are much lower than that of SJ-IC, indicating high charge mobility in these molecules. DL6 and DL7 have shown considerably improved open circuit voltage (VOC), reaching 1.49 eV and 1.48 eV, respectively. Thus, the modification strategy employed herein has been fruitful with productive effects, including better tuning of the energy levels, lower bandgaps, broader absorption, improved charge mobility, and increased VOC. Based on these results, it can be suggested that these newly presented molecules can be considered for practical applications in the future.

Synthesis and Antioxidant Studies of 2,4-Dioxothiazolidine-5-acetic Acid Based Organic Salts: SC-XRD and DFT Approach

In the current study, two organic salts (1 and 2) are synthesized, and then crystalline structures are characterized by FTIR, UV spectroscopy, and X-ray crystallographic studies. The organic salts 1 and 2 are optimized at the M06/6-311G(d,p)level of theory and further utilized for analysis of natural bond orbitals (NBOs), natural population, frontier molecular orbitals (FMOs), and global reactivity parameters, which confirmed the stability of the studied compounds and charge transfer phenomenon in the studied compounds. The studies further revealed that 1 and 2 are more stable than 3. The lowest energy merged monomer-coformer conformations were docked as flexible ligands with rigid fungal proteins and DNA receptors. The stagnant binding of the monomer through two H bonds with protein was observed for ligands 1 and 3 while different pattern was found with 2. The coformers formed a single H bond with the active site in 2 and 3 and a single pi-arene H interaction in 1. The two-point ligand-receptor interactions hooked the monomer between DNA base pairs for partial intercalation; pi stacking with additive hydrogen bonding with the base pair led to a strong benzimidazole interaction in 1 and 2, whereas ethylene diamine formed weak H bonding. Thus, the molecular docking predicted that the coformer exhibited DNA intercalation reinforced by its salt formation with benzimidazole 1 and methyl benzimidazole 2. Antioxidant studies depicted that 3 has a higher IC50 value than that of 2,4-D and also the largest value among the studied compounds, whereas 2 showed the lowest value among the studied compounds.

New Benzotrithiophene-Based Molecules as Organic P-Type Semiconductor for Small-Molecule Organic Solar Cells

A new benzotrithiophene-based small molecule, namely 2,5,8-Tris[5-(2,2-dicyanovinyl)-2-thienyl]-benzo[1,2-b:3,4-b':6,5-b″]-trithiophene (DCVT-BTT), was successfully synthesized and subsequently characterized. This compound was found to present an intense absorption band at a wavelength position of ∼544 nm and displayed potentially relevant optoelectronic properties for photovoltaic devices. Theoretical studies demonstrated an interesting behavior of charge transport as electron donor (hole-transporting) active material for heterojunction cells. A preliminary study of small-molecule organic solar cells based on DCVT-BTT (as the P-type organic semiconductor) and phenyl-C61-butyric acid methyl ester (as the N-type organic semiconductor) exhibited a power conversion efficiency of 2.04% at a donor: acceptor weight ratio of 1:1.

The Electronic Properties of Cadmium Naphthalene Diimide Coordination Complex

The computational simulations for electronic properties of cadmium (Cd) coordinated L-alanine NDI ligand (H₂-l-ala NDI) based complex are the focus of this research. For the first time, the Cd-NDI complex (monomer) has been produced using water as the solvent; this is a new approach to synthesizing the Cd-NDI complex that has not been reported yet. Along with crystallography and Hirsch field analysis, CAM-B3LYP/LANL2DZ and B3LYP/LANL2MB basis sets were used, and in-depth characterisation of the Cd-NDI complex by following DFT and TD-DFT hypothetical simulations. Hyperpolarizabilities, frontier molecular orbitals (FMOs), the density of states (DOS), dipole moment (µ), electron density distribution map (EDDM), transition density matrix (TDM), molecular electrostatic potential (MEP), electron-hole analysis (EHA), and electrical conductivity (σ) have all been studied regarding the Cd-NDI complex. The vibrational frequencies and types of interaction are studied using infrared (IR) and non-covalent interaction (NCI) analysis with iso-surface. In comparison to the Cd-NDI complex with 2.61, 2.42 eV E_g (using CAM-B3LYP/LANL2DZ and B3LYP/LANL2MB basis sets, respectively) and 376 nm λ_max, (in case of B3LYP/LANL2MB λ_max is higher), H₂-l-ala NDI have 3.387 eV E_g and 375 nm λ_max, metal-ligand coordination in complex dramatically altered charge transfer properties, such as narrowing band gap (E_g). Based on the electronic properties analysis of Cd-NDI complex, it is predicted that the Cd-NDI complex will have a spectacular (nonlinear optical) NLO response. The Cd-NDI complex is discovered to be advantageous for the creation of future nanoscale devices due to the harmony between the Cd metal and H₂-l-ala NDI, in addition to their influences on NLO characteristics.

Designing Electron-Deficient Diketone Unit Based Non-Fused Ring Acceptors with Amplified Optoelectronic Features for Highly Efficient Organic Solar Cells: A DFT Study

Organic solar cells (OSCs) made of electron-acceptor and electron-donor materials have significantly developed in the last decade, demonstrating their enormous potential in cutting-edge optoelectronic applications. Consequently, we designed seven novel non-fused ring electron acceptors (NFREAs) (BTIC-U1 to BTIC-U7) using synthesized electron-deficient diketone units and reported end-capped acceptors, a viable route for augmented optoelectronic properties. The DFT and TDDFT approaches were used to measure the power conversion efficiency (PCE), open circuit voltage (Voc), reorganization energies (λ_h, λ_e), fill factor (FF), light harvesting efficiency (LHE) and to evaluate the potential usage of proposed compounds in solar cell applications. The findings confirmed that the photovoltaic, photophysical, and electronic properties of the designed molecules BTIC-U1 to BTIC-U7 are superior to those of reference BTIC-R. The TDM analysis demonstrates a smooth flow of charge from the core to the acceptor groups. Charge transfer analysis of the BTIC-U1:PTB7-Th blend revealed orbital superposition and successful charge transfer from HOMO (PTB7-Th) to LUMO (BTIC-U1). The BTIC-U5 and BTIC-U7 outperformed the reference BTIC-R and other developed molecules in terms of PCE (23.29% and 21.18%), FF (0.901 and 0.894), normalized Voc (48.674 and 44.597), and Voc (1.261 eV and 1.155 eV). The proposed compounds enclose high electron and hole transfer mobilities, making them the ideal candidate for use with PTB7-Th film. As a result, future SM-OSC design should prioritize using these constructed molecules, which exhibit excellent optoelectronic properties, as superior scaffolds.

In Silico Designing of Thieno[2,3-b]thiophene Core-Based Highly Conjugated, Fused-Ring, Near-Infrared Sensitive Non-fullerene Acceptors for Organic Solar Cells

The performance of organic solar cells (OSCs) has been improving steadily over the last few years, owing to the optimization of device fabrication, fine-tuning of morphology, and thin-film processing. Thiophene core containing fused ring-type non-fullerene acceptors (NFAs) achieved significant proficiency for highly efficient OSCs. Quantum chemical computations are utilized herein with the motive of suggesting new NIR sensitive, highly efficient low-band gap materials for OSCs. A series of extended conjugated A-π-D-π-A architectured novel fused-ring NFAs (FUIC-1-FUIC-6) containing thieno[2,3-b]thiophene-based donor core are proposed by substituting the end-capped units of synthesized molecule F10IC. Different properties including frontier molecular orbital analysis, density of states analysis, transition density matrix analysis, excitation energy, reorganizational energies of both holes (λ_h) and electrons (λ_e), and open-circuit voltage (V _oc) were performed employing the density functional theory approach. Charge transfer analysis of the best-designed molecule with the donor complex was analyzed to comprehend the efficiency of novel constructed molecules (FUIC-1-FUIC-6) and compared with the reference. End-caped acceptor alteration induces the reduction of the energy gap between HOMO-LUMO (1.88 eV), tunes the energy levels, longer absorption in the visible and near-infrared regions, larger V _oc, smaller reorganizational energies, and binding energy values in designed structures (FUIC-1-FUIC-6) in comparison to reference (FUIC). The designed molecules show the best agreement with the PTBT-T donor polymer blend and cause the highest charge from the HOMO to the LUMO orbital. Our findings predicted that thieno[2,3-b] thiophene-based newly designed molecules would be efficient NFAs with outstanding photovoltaic characteristics and can be used in future applications of OSCs.

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

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.

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.