基于长短时记忆神经网络的简单语言模型学习分子动力学
Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network
摘要：
递归神经网络（RNN）已经在自然语言处理和语音识别方面取得了突破，其中数亿人每天通过智能手机，电子邮件服务器和其他途径使用这些工具。在这项工作中，我们展示了这样的RNN，特别是长短期记忆（LSTM）神经网络，也可以用于捕获化学和生物物理中出现的典型轨迹的时间演化。具体来说，我们使用基于LSTM的字符级语言模型。这从由更高维度系统的分子动力学模拟生成的1维随机轨迹学习概率模型。我们表明，该模型不仅可以捕获系统的玻尔兹曼统计，但它也重现动力学在一个大的时间尺度谱。我们演示了如何嵌入层，介绍了原来的上下文意义的单词或字符，在这里表现出一个非平凡的连接不同的亚稳态在底层的物理系统。我们通过不同的基准系统和多态核糖开关的单分子力谱轨迹证明了我们的模型和解释的可靠性。我们预计，我们的工作代表了理解和使用RNN建模和预测复杂随机分子系统动力学的垫脚石。
英文摘要：
Recurrent neural networks (RNNs) have led to breakthroughs in natural language processing and speech recognition, wherein hundreds of millions of people use such tools on a daily basis through smartphones, email servers and other avenues. In this work, we show such RNNs, specifically Long Short-Term Memory (LSTM) neural networks can also be applied to capturing the temporal evolution of typical trajectories arising in chemical and biological physics. Specifically, we use a character-level language model based on LSTM. This learns a probabilistic model from 1-dimensional stochastic trajectories generated from molecular dynamics simulations of a higher dimensional system. We show that the model can not only capture the Boltzmann statistics of the system but it also reproduce kinetics at a large spectrum of timescales. We demonstrate how the embedding layer, introduced originally for representing the contextual meaning of words or characters, exhibits here a nontrivial connectivity between different metastable states in the underlying physical system. We demonstrate the reliability of our model and interpretations through different benchmark systems and a single molecule force spectroscopy trajectory for multi-state riboswitch. We anticipate that our work represents a stepping stone in the understanding and use of RNNs for modeling and predicting dynamics of complex stochastic molecular systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
