Green James D, Fuemmeler Eric G, Hele Timothy J H
Department of Chemistry, University College London, Christopher Ingold Building, London WC1H 0AJ, United Kingdom.
Baker Laboratory, Cornell University, 259 East Avenue, Ithaca, New York 14853, USA.
J Chem Phys. 2022 May 14;156(18):180901. doi: 10.1063/5.0082311.
The discovery of molecules with tailored optoelectronic properties, such as specific frequency and intensity of absorption or emission, is a major challenge in creating next-generation organic light-emitting diodes (OLEDs) and photovoltaics. This raises the following question: How can we predict a potential chemical structure from these properties? Approaches that attempt to tackle this inverse design problem include virtual screening, active machine learning, and genetic algorithms. However, these approaches rely on a molecular database or many electronic structure calculations, and significant computational savings could be achieved if there was prior knowledge of (i) whether the optoelectronic properties of a parent molecule could easily be improved and (ii) what morphing operations on a parent molecule could improve these properties. In this Perspective, we address both of these challenges from first principles. We first adapt the Thomas-Reiche-Kuhn sum rule to organic chromophores and show how this indicates how easily the absorption and emission of a molecule can be improved. We then show how by combining electronic structure theory and intensity borrowing perturbation theory we can predict whether or not the proposed morphing operations will achieve the desired spectral alteration, and thereby derive widely applicable design rules. We go on to provide proof-of-concept illustrations of this approach to optimizing the visible absorption of acenes and the emission of radical OLEDs. We believe that this approach can be integrated into genetic algorithms by biasing morphing operations in favor of those that are likely to be successful, leading to faster molecular discovery and greener chemistry.
发现具有定制光电特性的分子,如特定的吸收或发射频率及强度,是制造下一代有机发光二极管(OLED)和光伏器件的一项重大挑战。这引发了以下问题:我们如何从这些特性预测潜在的化学结构?试图解决这个逆向设计问题的方法包括虚拟筛选、主动机器学习和遗传算法。然而,这些方法依赖于分子数据库或大量的电子结构计算,如果事先知道(i)母体分子的光电特性是否易于改善,以及(ii)对母体分子进行何种变形操作可以改善这些特性,就可以显著节省计算量。在这篇观点文章中,我们从第一性原理出发应对这两个挑战。我们首先将托马斯 - 赖歇 - 库恩求和规则应用于有机发色团,并展示其如何表明分子的吸收和发射能够被改善的难易程度。然后我们展示如何通过结合电子结构理论和强度借用微扰理论来预测所提出的变形操作是否会实现所需的光谱改变,从而推导出广泛适用的设计规则。我们接着提供了这种方法的概念验证示例,用于优化并苯的可见光吸收和自由基OLED的发射。我们相信,通过偏向于可能成功的变形操作,这种方法可以集成到遗传算法中,从而实现更快的分子发现和更绿色的化学。