作为序列生成问题的粗粒聚合物光谱预测：循环神经网络解决方案
Prediction of optical spectra of coarse-grained polymers as a sequence generation problem: the Recurrent Neural Networks solution
摘要：
共轭聚合物的粗粒度模拟已经成为研究有机光致发光器件物理的一种流行方法。虽然紫外-可见光谱仍然是询问这些设备的关键实验方法之一，但粗粒度模拟和光谱之间的严格桥梁从未建立。在这里，我们通过开发一种方法来解决这一挑战，该方法直接从粗粒度表示预测共轭聚合物的光谱，同时避免自组织程序，如从粗粒度到原子表示的反向映射，然后使用标准量子化学方法计算光谱。我们的方法基于生成式深度学习模型：长短期记忆递归神经网络（LSTM-RNN），这是由自然语言与聚合物构象的小波动引起的激发态能量的微扰扩展的数学结构之间的明显相似性提出的。我们使用这个模型来证明在粗粒度表示和后映射获得的光谱之间的危险差异。这表明，标准协议可能需要额外的微调，以成为可靠的，我们的模型提出了一种新的工具，独特的适合于改善回映射协议，并包括光谱数据的发展粗粒度的潜力。
英文摘要：
Coarse-grained simulations of conjugated polymers have become a popular way of investigating the device physics of organic photovoltaics. While UV-Vis spectroscopy remains one of key experimental methods for the interrogation of these devices, a rigorous bridge between coarse-grained simulations and spectroscopy has never been established. Here we address this challenge by developing a method that predicts spectra of conjugated polymers directly from coarse-grained representations while avoiding ad-hoc procedures such as back-mapping from coarse-grained to atomistic representations followed by computing the spectra using standard quantum chemistry methods. Our approach is based on a generative deep learning model: the long-short-term memory recurrent neural network (LSTM-RNN) and it is suggested by the apparent similarity between natural languages and the mathematical structure of the perturbative expansions of the excited state energies due to small fluctuations of the polymer conformation. We use this model to demonstrate a dangerous discrepancy between the spectra obtained in the coarse-grained representation and after the back-mapping. This indicates that standard protocols may require additional fine-tuning in order to become reliable and that our model presents a novel tool uniquely suited for improving the back-mapping protocols and for including spectral data in the development of coarse-grained potentials.
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