Prediction of good reaction coordinates and future evolution of MD trajectories using Regularized Sparse Autoencoders: A novel deep learning approach
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利用正则化稀疏自编码器识别反应坐标并预测分子动力学轨迹
- 研究问题
- 如何从分子动力学轨迹中选取少量关键反应坐标，使其区分反应物与产物状态，并用于预测轨迹的未来演化？
- 方法与思路
- 作者使用正则化稀疏自编码器发现潜在反应坐标，将分子动力学轨迹视为多变量时间序列，进行多步预测。
- 材料陈述的贡献
- 摘要称，稀疏正则化帮助选择少量重要坐标，并以丙氨酸二肽及原黄素与 DNA 两个分子系统展示该方法。
- 阅读关注点
- 应核对坐标如何满足慢变量要求、稀疏惩罚和预测目标的设置、轨迹划分方式，以及多步误差和物理意义验证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未提供误差指标，也未证明学得坐标满足所述慢变量标准。
原文摘要
Identifying reaction coordinates(RCs) is an active area of research, given the crucial role RCs play in determining the progress of a chemical reaction. The choice of the reaction coordinate is often based on heuristic knowledge. However, an essential criterion for the choice is that the coordinate should capture both the reactant and product states unequivocally. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. Also, the coordinate should be the slowest one so that all the other degrees of freedom can easily equilibrate along the reaction coordinate. We used a regularised sparse autoencoder, an energy-based model, to discover a crucial set of reaction coordinates. Along with discovering reaction coordinates, our model also predicts the evolution of a molecular dynamics(MD) trajectory. We showcased that including sparsity enforcing regularisation helps in choosing a small but important set of reaction coordinates. We used two model systems to demonstrate our approach: alanine dipeptide system and proflavine and DNA system, which exhibited intercalation of proflavine into DNA minor groove in an aqueous environment. We model MD trajectory as a multivariate time series, and our latent variable model performs the task of multi-step time series prediction. This idea is inspired by the popular sparse coding approach - to represent each input sample as a linear combination of few elements taken from a set of representative patterns.