Convolutional Neural Networks for Long Time Dissipative Quantum Dynamics,The Journal of Physical Chemistry Letters

当前位置： X-MOL 学术 › J. Phys. Chem. Lett. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Convolutional Neural Networks for Long Time Dissipative Quantum Dynamics
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2021-03-05 , DOI: 10.1021/acs.jpclett.1c00079
Luis E. Herrera Rodríguez _{1,

2,

3} , Alexei A. Kananenka ₃

Affiliation

Exact numerical simulations of dynamics of open quantum systems often require immense computational resources. We demonstrate that a deep artificial neural network composed of convolutional layers is a powerful tool for predicting long-time dynamics of open quantum systems provided the preceding short-time evolution of a system is known. The neural network model developed in this work simulates long-time dynamics efficiently and accurately across different dynamical regimes from weakly damped coherent motion to incoherent relaxation. The model was trained on a data set relevant to photosynthetic excitation energy transfer and can be deployed to study long-lasting quantum coherence phenomena observed in light-harvesting complexes. Furthermore, our model performs well for the initial conditions different than those used in the training. Our approach reduces the required computational resources for long-time simulations and holds the promise for becoming a valuable tool in the study of open quantum systems.

中文翻译：

长时间耗散量子动力学的卷积神经网络

开放量子系统动力学的精确数值模拟通常需要大量的计算资源。我们证明了由卷积层组成的深层人工神经网络是预测开放量子系统的长期动力学的强大工具，前提是已知系统的先前短时演化。在这项工作中开发的神经网络模型可以在从弱阻尼相干运动到非相干弛豫的不同动力学状态下，高效，准确地模拟长期动力学。该模型在与光合作用激发能转移相关的数据集上进行了训练，可用于研究在光捕获复合物中观察到的持久量子相干现象。此外，我们的模型在与训练中使用的初始条件不同的初始条件下表现良好。

更新日期：2021-03-11

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11