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The alternant polycyclic aromatic hydrocarbon pyrene has photophysical properties that can be tuned with different donor and acceptor substituents. Recently, a D (donor)-Pyrene (bridge)-A (acceptor) system, DPA, with the electron donor N,N-dimethylaniline (DMA), and the electron acceptor trifluoromethylphenyl (TFM), was investigated by means of time-resolved spectroscopic measurements (J. Phys. Chem. Lett. 2021, 12, 2226-2231). DPA shows great promise for potential applications in organic electronic devices. In this work, we used the ab initio second-order algebraic diagrammatic construction method ADC(2) to investigate the excited-state properties of a series of analogous DPA systems, including the originally synthesized DPAs. The additionally investigated substituents were amino, fluorine, and methoxy as donors and nitrile and nitro groups as acceptors. The focus of this work was on characterizing the lowest excited singlet states regarding charge transfer (CT) and local excitation (LE) characters. For the DMA-pyrene-TFM system, the ADC(2) calculations show two initial electronic states relevant for interpreting the photodynamics. The bright S1 state is locally excited within the pyrene moiety, and an S2 state is localized ~0.5 eV above S1 and characterized as a donor to pyrene CT state. HOMO and LUMO energies were employed to assess the efficiency of the DPA compounds for organic photovoltaics (OPVs). HOMO-LUMO and optical gaps were used to estimate power conversion and light-harvesting efficiencies for practical applications in organic solar cells. Considering the systems using smaller D/A substituents, compounds with the strong acceptor NO2 substituent group show enhanced CT and promising properties for use in OPVs. Some of the other compounds with small substituents are also found to be competitive in this regard.
RESUMO
Hammett's constants σ quantify the electron donor or electron acceptor power of a chemical group bonded to an aromatic ring. Their experimental values have been successfully used in many applications, but some are inconsistent or not measured. Therefore, developing an accurate and consistent set of Hammett's values is paramount. In this work,we employed different types of machine learning (ML) algorithms combined with quantum chemical calculations of atomic charges to predict theoretically new Hammett's constants σm, σp, σm0, σp0, σp+, σp-, σR, and σI for 90 chemical donor or acceptor groups. New σ values (219), including previously unknown 92, are proposed. The substituent groups were bonded to benzene and meta- and para-substituted benzoic acid derivatives. Among the charge methods (Mulliken, Löwdin, Hirshfeld, and ChelpG), Hirshfeld showed the best agreement for most kinds of σ values. For each type of Hammett constant, linear expressions depending on carbon charges were obtained. The ML approach overall showed very close predictions to the original experimental values, with meta- and para-substituted benzoic acid derivative values showing the most accurate values. A new consistent set of Hammett's constants is presented, as well as simple equations for predicting new values for groups not included in the original set of 90.
RESUMO
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)-based σ m , σ p , σ m 0 , σ p 0 , σ p + , σ p - , σ I , and σ 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 â 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 the σ 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) reduced E 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.
