Abstract:In the present study, ten metal free non-linear optical (NLO) compounds have been designed. These compounds have designed by structural modification of (2-cyano-5-(4-(phenyl(4-vinylphenyl)amino)phenyl) penta-2,4-dienoic acid (TC4). Density functional theory was used for structure optimization and determination of photo-physical properties. These compounds contain triphenylamine as electron-donor and cyanoacrylic acid as acceptor. Five π-spacers are used to connect the donor and acceptor. Two auxiliary donors a… Show more
“…So far, most experimental work is the result of tedious and time‐consuming trial‐and‐error methods [8–11]. Experimentally, it is very difficult to explore large chemical space due to very expensive chemicals, difficult synthesis procedures, and tedious purification steps [12–14]. There is a continuous increase in the performance of OSCs, however, it took years.…”
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.
“…So far, most experimental work is the result of tedious and time‐consuming trial‐and‐error methods [8–11]. Experimentally, it is very difficult to explore large chemical space due to very expensive chemicals, difficult synthesis procedures, and tedious purification steps [12–14]. There is a continuous increase in the performance of OSCs, however, it took years.…”
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.
“…Therefore, it can be a challenging material for nonlinear optical applications. Thus, the second-harmonic generation features of the material at the molecular level are caused by the nonzero value of (μi) and the large value of (β) [ 39 , 40 ].…”
Section: Resultsmentioning
confidence: 99%
“…The flat zone on curved surfaces indicates π⋯π stacking with little overlap between adjacent molecules. In order to quantitatively investigate all potential non-covalent interactions that could be causing crystal packing, Hirshfeld surface analysis is used [ 39 , 40 ]. Figure 5 100% fingerprint of DMTHO.…”
“…108,109 Based above electronic transition analysis, quinoxaline-fused [7]carbohelicene derivatives (compounds 2-4) exhibit intramolecular charge transfer. [110][111][112][113][114][115] Thus, the first hyperpolarizability (β HRS ) values of the studied compounds were calculated at CAM-B3LYP/6-31+G(d) level and were given in Table 2. Strong electron donor (NH 2 ) or electron acceptor (NO 2 ) groups were also introduced to probe the charger transfer cooperativity and find the effective way to enhance NLO response.…”
“…Recently, DFT calculations have emerged as a powerful tool for the investigation and prediction of compounds with large NLO responses. [110][111][112][113][114][115] Thus, the rst hyperpolarizability (b HRS ) values of the studied compounds were calculated at the CAM-B3LYP/6-31+G(d) level and are given in Table 2. The calculated b HRS values of these studied compounds are larger than those of the typical organic NLO compounds.…”
Helicene and its derivatives have received much attention as the candidates of organic photoelectronic material. Recently, novel quinoxaline-fused [7]carbohelicene derivatives exhibit unique structural and photophysical properties, especially in the crystal states. However, their structure-property relationships have not been fully understood from the micromechanism, which is also important to further improve their performance. Here, the electronic transition, electronic circular dichroism (CD), second-order nonlinear optical (NLO) response and charge transport properties of five quinoxaline-fused [7]carbohelicene derivatives have been investigated based on density functional theory calculations. The experimental UV-Vis/CD spectra of the studied compounds were well reproduced by our calculations. Thus, we can assign their electron transition properties and absolution configurations (ACs) with high confidence. It is found that CD bands of quinoxaline-fused [7]carbohelicene derivatives mainly originate from exciton coupling between quinoxaline, phenyl or 4-methoxyphenyl and [7]carbohelicene, which is in sharp contrast to [7]carbohelicene. More interestingly, these derivatives possess large first hyperpolarizability values. For example, the βHRS value of compound 6 is 32.96 × 10 −30 esu, which is about 190 times larger than that of the organic urea molecule. The bandwidth of valence band of compound 2 is comparable to that of conduction band and slightly larger than that of tris(8-hydroxyquinolinato)aluminium. This means that compound 2 is the potential candidate as ambipolar charge transport material.
Please do not adjust marginsPlease do not adjust margins modulates the LUMO energy level. As a consequence, LUMO energy of compound 2 is much lower than that of compound 1 (Table S6), which leads to the smaller energy gap (Eg) for compound 2. Moreover, the main electron transitions characters are of charge transfer from quinoxaline to [7]carbohelicene and vice versa (Fig. 3). On the basis of above analysis, it is found that involvement of quinoxaline unit not only modulates the electron absorption wavelength but also alter the electron transition property. Compounds 3 and 4 also exhibit four absorption bands, which is similar to those of compound 2. In other words, the substituent effect of phenyl or 4-methoxyphenyl group on the UV-Vis spectra of compound 2 is not great. It is noted that some of the molecular orbitals involved in these transitions partially located on the phenyl or 4-methoxyphenyl group.
Fig. 2Calculated UV-Vis (left) and CD (right) spectra in gas phase of 1, 2, 3 and 4 at the TDB3LYP/6-31+G(d) level of theory along with the experimental spectra (red dashed line).
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