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Discovery of High-Performance Organic Non-Linear Optical Molecules by Systematic 'Smart Material' Design Strategies

机译:通过系统的“智能材料”设计策略发现高性能有机非线性光学分子

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This paper presents the discovery of a range of high-performance organic non-linear optical (NLO) materials, that arises from 'smart material' design and systematic search strategies. This systematization circumvents the previous use of iterative discovery methods, which can only ever afford incremental improvements to currently known NLO materials, and they have no capacity to reveal entirely new classes of suitable NLO materials. This new approach employs data-mining, using the world's repository of all published organic crystal structures as a representative set of chemical space. Two independent search strategies are implemented, each predicting the best organic NLO materials. The first search method relies on the concept of 'molecular lego', taking particular types of molecular fragments that are known to be important constituents of an NLO active material (the 'lego'), and searching for these through chemical space, with the assistance of graph theory algorithms and systematic enumeration and classification. The second search method uses quantummechanical calculations to evaluate the molecular hyperpolarizability, β, of every organic molecule in the aforementioned database. Since β affords the intrinsic measure of NLO output, all organic molecules listed in descending order of β values reflects a ranked list of their NLO potential. The NLO properties of selected materials that are highly-ranked in these two lists were then tested experimentally, using Hyper-Rayleigh Scattering (HRS). The predictions are shown to be borne out by such experiments: HRS results show β_0 (static hyperpolarizability) values that are up to 10 x greater than those for the industrial reference Disperse Red 1. Due to the commercial potential of these results, four new classes of NLO materials identified by this study have recently been patented.
机译:本文介绍了一系列高性能有机非线性光学(NLO)材料,由“智能材料”设计和系统搜索策略产生。这种系统化避免了以前使用迭代发现方法,这只能为目前已知的NLO材料提供增量的改进,它们没有能力揭示完全新的合适NLO材料。这种新方法采用了数据挖掘,使用全球所有已公布的有机晶体结构的存储库作为代表性化学空间。实施了两个独立的搜索策略,每次预测最佳有机NLO材料。第一个搜索方法依赖于“分子乐高”的概念,采取特定类型的分子片段,该分子片段是NLO活性物质(“乐高”的重要组成部分,并通过辅助来搜索这些通过化学空间图中的图解算法和系统枚举和分类。第二种搜索方法使用量子力学计算来评估上述数据库中每种有机分子的分子超极化性,β。由于β提供NLO输出的内在测量,因此以β值的降序列出的所有有机分子反映了其NLO潜力的排名列表。然后使用超瑞利散射(HRS)实验测试在这两个列表中高度排名的所选材料的NLO性质。这些实验证明了预测结果:HRS结果显示高达10 x的β_0(静态超极化性)值大于工业参考分散红色的10 x。由于这些结果的商业潜力,四个新课程本研究确定的NLO材料最近得到了专利。

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