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We present an improved inverse-design approach for automatically identifying molecular (or other) systems with optimal values for prechosen properties. The new approach uses SMILES (simplified molecular input line entry system) to describe molecular structures efficiently, a genetic algorithm to optimize the molecules automatically, and the DFTB+ (self-consistent charge density functional tight-binding) method to calculate electronic properties. Thereby, almost every class of materials─even macromolecules or monomers─can be studied easily. Without crossover operators but with only mutation operators, the genetic algorithm is more adaptive to SMILES while keeping its efficiency. DFTB+ is more accurate than the DFTB method used in our previous inverse-design approach for the study of excited states and charge transfer processes. The improved approach is applied to optimize benzene, pyridine, pyridazine, pyrimidine, and pyrazine derivatives for seven electronic properties, which all are highly relevant and important for the performance of molecules in solar cells. We found that for some electronic properties, the precise composition and structure of the backbone have remarkable impacts on the value of the electronic properties and/or on the set of functional groups that leads to the best performance. On the contrary, for other properties, these effects are less pronounced. The reasonable optimal functional groups and/or substitution patterns are reported.

The Harris Hawks Optimization Algorithm is a new metaheuristic optimization that simulates the process of Harris Hawk hunting prey (rabbit) in nature. The global and local search processes of the algorithm are performed by simulating several stages of cooperative behavior during hunting. To enhance the performance of this algorithm, in this paper we propose a neighborhood centroid opposite-based learning Harris Hawks optimization algorithm (NCOHHO). The mechanism of applying the neighborhood centroid under the premise of using opposite-based learning technology to improve the performance of the algorithm, the neighborhood centroid is used as a reference point for the generation of the opposite particle, while maintaining the diversity of the population and make full use of the swarm search experience to expand the search range of the reverse solution. Enhancing the probability of finding the optimal solution and the improved algorithm is superior to the original Harris Hawks Optimization algorithm in all aspects. We apply NCOHHO to the training of feed-forward neural network (FNN). To confirm that using NCOHHO to train FNN is more effective, five classification datasets are applied to benchmark the performance of the proposed method. Comprehensive comparison and analysis from the three aspects of mean, variance and classification success rate, the experimental results show that the proposed NCOHHO algorithm for optimization FNN has the best comprehensive performance and has more outstanding performance than other metaheuristic algorithms in terms of the performance measures.

An oligo(phenylenevinylene) bridged intramolecular charge-transfer (ICT) compound, (TCNQ)2OPV3, has been synthesized and its third- and fifth-order nonlinear optical refraction indexes have been determined by measurement with the 4f system with a phase-object, under near-infrared excitation.

Dye-sensitized solar cells (DSSCs) have attracted much interest during the past few decades. However, it is still a tremendous challenge to identify organic molecules that give an optimal power conversion efficiency (PCE). Here, we apply our recently developed, inverse-design method for this issue with the special aim of identifying porphyrins with promisingly high PCE. It turns out that the calculations lead to the prediction of 15 new molecules with optimal performances and for which none so far has been studied. These porphyrin derivatives will in the near future be synthesized and subsequently tested experimentally. Our inverse-design approach, PooMa, is based on the strategy of providing suggestions for molecular systems with optimal properties. PooMa has been developed as a tool that requires minimal resources and, therefore, builds on various approximate methods. It uses genetic algorithm to screen thousands (or often more) of molecules. For each molecule, the density-functional tight-binding (DFTB) method is used for calculating the electronic properties. In the present work, five different electronic properties are determined, all of which are related to optical performance. Subsequently, a quantitative structure-property relationship (QSPR) model is constructed that can predict the PCE through those five electronic properties. Finally, we benchmark our results through more accurate DFT calculations that give further information on the predicted optimal molecules.

The real-valued moth-flame optimization algorithm (MFO) is a new bio-inspired algorithm. It simulates the navigation mechanism of moth lateral positioning under moonlight. MFO has excellent performance in solving optimization problems and has strong ability in solving power optimization combination. In order to improve the global search ability of the algorithm, a complex-valued encoding moth-flame optimization algorithm (CMFO) is proposed. The real and imaginary parts of the population are updated by using the diploid structure of complex-valued encoding. The diversity of the population was increased. The effectiveness of CMFO algorithm has been verified by 4 benchmark problems. Statistically significant results and analysis show that the proposed complex-valued encoding moth-flame optimization algorithm is very promising and occasionally competitive compared with other well-established meta-heuristic techniques.

Using our recently developed theoretical inverse-design method (PooMa) a new series of branched oligothiophene molecules for photovoltaic donors are designed. PooMa uses a genetic algorithm to screen a huge pool of compounds combined with a fast electronic-structure method (Density-Functional Tight-Binding, DFTB) with reasonable accuracy. Here, we apply this inverse-design method to identify a set of 20 branched oligothiophene systems with promisingly high efficiencies by using a Quantitative Structure Property Relation (QSPR) model based on five electronic descriptors that describe the performance of organic solar cells. We consider a pool of oligomers that are modified by attaching to each of 7 different sites one out of 22 functional groups, i.e., a pool of 227 ≈ 2.5×109 molecules. Subsequently, density-functional-theory (DFT) and Time-Dependent-DFT (TD-DFT) calculations in the gas phase with a 6-31G(d,p) basis set have been carried through to give further information on the suggested oligomers. Bulk-heterojunction photovoltaic cells were designed with the suggested oligothiophenes as donors and Phenyl-C61-butyric acid methyl (PCBM) derivatives as acceptors. Conversion efficiencies of the designed photovoltaic cells were examined with the Scharber diagram model.

Small organic conjugated molecules are key elements for low-cost photovoltaic devices. One example is cyanopyridone molecules. By modifying these molecules, for instance through optimally chosen functional groups attached to the backbone, their properties can be improved. However, the very large number of possible modifications makes it difficult to identify the best performing molecules. In the present work, we have used a computational inverse-design approach (PooMa) to identify the positions and types of functional groups attached to a modified cyanopyridone that lead to the best performance in solar-energy harvesting. A QSPR model based on five electronic descriptors has been used to determine the properties of solar cells. Our approach uses a genetic algorithm to search the chemical space containing 184 (104,976) substituted cyanopyridone systems and predicts out of those the best 20 molecules with optimal performance efficiencies (PCE). PooMa uses the Density-Functional Tight-Binding (DFTB) method for calculating the electronic properties. DFTB is a fast method with acceptable accuracy and, therefore, can be used on a normal desktop without expensive hard- or software. In order to get further information about our suggested systems, a DFT method and its derivative TD-DFT are applied.

Three π-A-porphyrins containing long alkoxyl chains attached to the ortho position of phenyl ring and a phenyl carboxylate acid or acrylic acid at the meso position of porphyrin were synthesized. All compounds were characterized by 1H NMR and mass spectrometry. Optical and electrochemical properties were also obtained. The photovoltaic properties of these π-A-porphyrins were examined for the first time and sensitizers N-1 and N-3 achieved comparable light to electricity conversion efficiencies: 3.94% for N-1 and 4.14% for N-3. However, preparation of N-1 required simple and cost-effective synthesis which made it a promising candidate for the future practical DSSC applications. The low efficiency conversion of N-2 was well explained by the amount of dye loading, IPCE and EIS.

A series of novel zinc porphyrins which are featured with a donor–π–acceptor structure have been synthesized for use in the dye-sensitized solar cells. Various diarylamine moiety, such as diphenylamine, iminodibenzyl or iminostilbene, is introduced at porphyrin meso position as an electron donating group. The cell fabricated with the iminodibenzyl-substituted porphyrin sensitizer yields a short circuit photocurrent density of 9.68 mA/cm2, an open-circuit voltage of 740 mV, and a fill factor of 73.48%, corresponding to an overall conversion efficiency (η) up to 5.26%, which is greater than those obtained by diphenylamine- and iminostilbene-substituted porphyrin-sensitized solar cells (η = 4.05% and 2.62%, respectively). The theoretical studies reveal that the iminodibenzyl donor has the strongest electron donating ability among all three diarylamine substituents employed, which is believed to play a significant role in influencing the photovoltaic properties of these sensitizer-based solar cells.

Four phenylethynyl-porphyrins 4a, 4b, 5a, and 5b with either one (4a and 4b) or two (5a and 5b) phenylethynyl groups attached at C10 or/and C20 position of the porphyrin ring (defined as the axial direction), and with phenyl (4a and 5a) or 2,6-dialkoxylphenyl group (4b and 5b) at C5 position of porphyrin in a push–pull framework (defined as the push–pull direction) were designed and synthesized. The synthesized porphyrins were applied in dye-sensitized solar cells (DSSCs). Unexpectedly, although the light harvesting capability of the dye is enhanced with the increase of the number of phenylethynyl group as indicated in the UV–Vis spectra, the light to electricity conversion efficiency (η) of the corresponding solar cell drops. The influence of the number of the phenylethynyl group present along the axial direction of the porphyrin on the photovoltaic performance of DSSCs is well explained by the condensed Fukui function obtained via DFT calculations.

Contains fulltext : 166670.pdf (Publisher’s version ) (Closed access) The discovery of the phenomenon known as aggregation-induced emission (AIE) has opened the door to a variety of brilliant organic solid-state light-emitting materials. While AIE is well established in linear optics, the development of AIE luminogens (AIEgens) with highly efficient nonlinear optical (NLO) effects remains relatively unexplored. Particularly, second-order NLO requires the AIEgens to be organized in a non-centrosymmetric fashion, and such examples are rarely reported. Here, an AIEgen, 2,7-di([1,1-biphenyl]-4-yl)-fluorenone (4-DBpFO), is designed and synthesized by introducing a carbonyl group onto the backbone of p-hexaphenylene. The restricted rotation of the compound upon aggregation results in a dramatic enhancement of the linear optical emission when forming self-assemblies. Furthermore, introducing the carbonyl group drives the formation of hydrogen bonded molecular chains, which are attached by the zigzag CH interactions in a non-centrosymmetric way. As a result, the dipole of each individual molecule contributes accumulatively to a macroscopic dipole of the formed 4-DBpFO microcrystals. This leads to a highly efficient second harmonic generation with very high laser damage treshold. This AIEgen, whose optical response is greatly enhanced in both linear and nonlinear optical regimes upon the formation of well-defined self-assemblies, has potential applications in next generation photonic circuits.

We present an inverse-design method, the poor man's materials optimization, that is designed to identify materials within a very large class with optimized values for a pre-chosen property. The method combines an efficient genetic-algorithm-based optimization, an automatic approach for generating modified molecules, a simple approach for calculating the property of interest, and a mathematical formulation of the quantity whose value shall be optimized. In order to illustrate the performance of our approach, we study the properties of organic molecules related to those used in dye-sensitized solar cells, whereby we, for the sake of proof of principle, consider benzene as a simple test system. Using a genetic algorithm, the substituents attached to the organic backbone are varied and the best performing molecules are identified. We consider several properties to describe the performance of organic molecules, including the HOMO-LUMO gap, the sunlight absorption, the spatial distance of the orbitals, and the reorganisation energy. The results show that our method is able to identify a large number of good candidate structures within a short time. In some cases, chemical/physical intuition can be used to rationalize the substitution pattern of the best structures, although this is not always possible. The present investigations provide a solid foundation for dealing with more complex and technically relevant systems such as porphyrins. Furthermore, our "properties first, materials second" approach is not limited to solar-energy harvesting but can be applied to many other fields, as briefly is discussed in the paper.

A series of D–π–A zinc porphyrin sensitizers Dye1–Dye6 bearing a substituted iminodibenzyl group at the porphyrin meso position, which is expected to have different electron-donating abilities, were designed. Theoretical studies were performed to examine the photovoltaic properties of these molecules in dye-sensitized solar cells (DSSCs). In particular, the important concepts, the Fukui function and the extended condensed Fukui function, are employed to describe the electron-donating abilities accurately at the quantitative level. Tangui Le Bahers model was adopted to analyze charge transfer (CT) during electron transition. A correlation between the electron donating abilities of the derived iminodibenzyl group and CT was built to evaluate the cell performance based on sensitizers Dye1–Dye6. The theoretical studies showed that porphyrins Dye1–Dye3 bearing an extremely strong electron-donating group (EDG) would fail in the generation of photocurrent in the closed circuit when applied in DSSCs due to the higher level of the HOMO energy than the redox potential of the redox couple (I−/I3−). The one with a weaker EDG (Dye4) is expected to show better photovoltaic performance than porphyrin IDB with an unsubstituted iminodibenzyl group. This study demonstrates a reliable method involving the employment of the Fukui function, the extended condensed Fukui function and the Tangui Le Bahers model for the evaluation of newly designed D–π–A type porphyrin sensitizers for use in DSSCs, and as guidance for future molecular design.