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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.

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.