论文标题
使用简单的语言模型学习分子动力学，建立在长期短期记忆神经网络上
Learning Molecular Dynamics with Simple Language Model built upon Long Short-Term Memory Neural Network
论文作者
论文摘要
经常性的神经网络（RNN）导致了自然语言处理和语音识别的突破，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每天使用此类工具。在这项工作中，我们显示了此类RNN，特别是长期短期记忆（LSTM）神经网络也可以应用于捕获化学和生物物理学中典型轨迹的时间演变。具体来说，我们使用基于LSTM的字符级语言模型。这从一维随机轨迹中学习了一个概率模型，该轨迹是由高维系统的分子动力学模拟产生的。我们表明，该模型不仅可以捕获系统的Boltzmann统计数据，而且还可以在大量的时间标准下再现动力学。我们演示了最初是为了表示单词或字符的上下文含义引入的嵌入层如何在这里展示了基础物理系统中不同亚稳态状态之间的非平凡连通性。我们通过不同的基准系统以及用于多态核糖开关的单分子力光谱轨迹来证明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来建模和预测复杂随机分子系统动力学的垫脚石。
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.
