Robust design strategy using a scaffold based Turing machine model--- Application to PDI based dyes

Feng Wang , Vladislav Vasilyev
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Abstract

This study turns the design and screen of new compounds into a computer integer crunch of the control arrays using a scaffold based Turing machine model. If small organic fragments are stored in a fragment database (FDB) in which each fragment is labelled by an integer in an array, the position and frequency of the integer control how the fragment clicks on a scaffold (template compound). This method can robustly screen a large number of candidate fragments for solar cells and other applications such as drug design with minimal human assistance. As a proof of concept, we consider terminal imide substituents on the core perylene diimide (PDI) to develop PDI derivatives capable of absorbing UV–vis light for solar cell applications. Time dependent-density functional theory (TD-DFT) method was employed in the calculations. When the imide substituents are electron donors such as azobenzene (DPI-7), they produce a larger bathochromic shift (Δλmax) from the core DPI band position. The UV–vis absorption transitions of these DPI derivatives have more charge transfer (CT) character, as the highest occupied molecular orbitals (HOMO) are located on the fragments rather than the core DPI region. Our study presents a robust and efficient high-performance organic dye screen design strategy, and further research in DPI-based solar cell design will focus on promoting the HOMO to LUMO transitions of the optical spectra.

基于支架的图灵机模型鲁棒设计策略——在PDI染料中的应用
本研究利用基于支架的图灵机模型,将新化合物的设计和筛选转化为控制阵列的计算机整数压缩。如果小的有机片段存储在片段数据库(FDB)中,其中每个片段用数组中的整数标记,整数的位置和频率控制片段如何在支架(模板化合物)上点击。这种方法可以在最少的人工辅助下,为太阳能电池和其他应用(如药物设计)筛选大量候选片段。作为概念的证明,我们考虑在核心苝二酰亚胺(PDI)上的末端亚胺取代基来开发能够吸收紫外-可见光的PDI衍生物,用于太阳能电池。计算采用时依赖密度泛函理论(TD-DFT)方法。当亚胺取代基是电子供体时,如偶氮苯(DPI-7),它们从核心DPI带位置产生较大的色移(Δλmax)。这些DPI衍生物的紫外-可见吸收跃迁具有更多的电荷转移(CT)特征,因为最高已占据分子轨道(HOMO)位于碎片上而不是DPI核心区域。我们的研究提出了一种稳健高效的高性能有机染料屏设计策略,基于dpi的太阳能电池设计的进一步研究将集中在促进光谱的HOMO到LUMO跃迁上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial intelligence chemistry
Artificial intelligence chemistry Chemistry (General)
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