Role of the Backbone when Optimizing Functional Groups─A Theoretical Study Based on an Improved Inverse-Design Approach.

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.

Application of an inverse-design method to optimizing porphyrins in dye-sensitized solar cells.

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.

From properties to materials: An efficient and simple approach.

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.

Application-oriented computational studies on a series of D-π-A structured porphyrin sensitizers with different electron-donor groups.

A series of D-π-A zinc porphyrin sensitizers 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 . The theoretical studies showed that porphyrins 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 () is expected to show better photovoltaic performance than porphyrin 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.

Optimizing small conjugated molecules for solar-cell applications using an inverse-design method.

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.