从稀疏数据中学习激发态

IF 7.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Jingkun Shen, Lucy E. Walker, Kevin Ma, James D. Green, Hugo Bronstein, Keith T. Butler and Timothy J. H. Hele
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引用次数: 0

摘要

发射性有机自由基目前因其在下一代高效有机发光二极管(OLED)器件和分子量子比特中的潜在应用而备受关注。然而,模拟它们的光电特性是具有挑战性的,主要是由于自旋污染和它们激发态的多构型特征。在这里,我们提出了一种数据驱动的方法,其中首次直接从实验激发态数据中学习有机自由基的激发态,使用比机器学习通常所需的数据少得多的数据。我们采用快速、自旋纯半经验方法ExROPPP计算自由基激发态,作为替代物理模型,学习最优参数集。为了实现这一目标,我们编译了已知最大的有机自由基几何数据库及其UV-vis数据,我们使用这些数据来训练我们的模型。我们训练的模型激发态能的均方根误差和平均绝对误差分别为0.24和0.16 eV,比文献参数的ExROPPP有了很大的提高。我们合成了四种新的有机自由基,并在它们的光谱上测试了模型,发现了更低的误差和与训练集相似的相关性。这为下一代基基光电子的高通量发现铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Learning radical excited states from sparse data

Learning radical excited states from sparse data

Emissive organic radicals are currently of great interest for their potential use in the next generation of highly efficient organic light emitting diode (OLED) devices and as molecular qubits. However, simulating their optoelectronic properties is challenging, largely due to spin-contamination and the multiconfigurational character of their excited states. Here we present a data-driven approach where, for the first time, the excited electronic states of organic radicals are learned directly from experimental excited state data, using a much smaller amount of data than typically required by Machine Learning. We adopt ExROPPP, a fast and spin-pure semiempirical method for the calculation of the excited states of radicals, as a surrogate physical model for which we learn the optimal set of parameters. To achieve this we compile the largest known database of organic radical geometries and their UV-vis data, which we use to train our model. Our trained model gives root mean square and mean absolute errors for excited state energies of 0.24 and 0.16 eV respectively, improving hugely over ExROPPP with literature parameters. Four new organic radicals are synthesised and we test the model on their spectra, finding even lower errors and similar correlation as for the training set. This paves the way for the high throughput discovery of next generation radical-based optoelectronics.

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来源期刊
Chemical Science
Chemical Science CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
14.40
自引率
4.80%
发文量
1352
审稿时长
2.1 months
期刊介绍: Chemical Science is a journal that encompasses various disciplines within the chemical sciences. Its scope includes publishing ground-breaking research with significant implications for its respective field, as well as appealing to a wider audience in related areas. To be considered for publication, articles must showcase innovative and original advances in their field of study and be presented in a manner that is understandable to scientists from diverse backgrounds. However, the journal generally does not publish highly specialized research.
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