论文标题
通过机器学习的中性多环芳烃的红外光谱
Infrared spectra of neutral polycyclic aromatic hydrocarbons by machine learning
论文作者
论文摘要
对多环芳烃（PAHS）的兴趣涵盖了许多领域和红外光谱法，通常是脱离其分子结构的选择方法。为了计算振动频率，许多理论研究采用量子计算方法或经验势，但是将第一种方法的准确性与第二种方法的计算成本相结合仍然很难。在这项工作中，我们采用了机器学习技术来开发基于人工神经网络（ANN）体系结构的势能表面和偶极映射。总的来说，虽然只接受了11个小PAH分子的训练，但所获得的ANN能够检索这些小分子的红外光谱，但更重要的是，与训练集不同的8个大PAH中，有8个大PAH，从而证明了我们方法的可传递性。
The Interest in polycyclic aromatic hydrocarbons (PAHs) spans numerous fields and infrared spectroscopy is usually the method of choice to disentangle their molecular structure. In order to compute vibrational frequencies, numerous theoretical studies employ either quantum calculation methods, or empirical potentials, but it remains difficult to combine the accuracy of the first approach with the computational cost of the second. In this work, we employed Machine Learning techniques to develop a potential energy surface and a dipole mapping based on an artificial neural network (ANN) architecture. Altogether, while trained on only 11 small PAH molecules, the obtained ANNs are able to retrieve the infrared spectra of those small molecules, but more importantly of 8 large PAHs different from the training set, thus demonstrating the transferability of our approach.
