本项目运用最近发展的路径形式的量子动力学方法：聚环（Ring Polymer）分子动力学（RPMD）方法、基于神经网络方法拟合的从头算全维势能面，研究燃烧体系中重要基元反应H+CH4->H2+CH3的速率常数。目前，理论与实验速率常数之间仍然存在比较大的偏差，需要发展新的计算技术，精确而便捷地计算反应速率。对燃烧机理的敏感分析显示，H+CH4体系是低级烷烃燃烧过程的敏感反应。本项目将以它的速率常数作为主要研究对象。精确计算基元反应速率常数需要建立在全维量子力学计算的基础上。但是多于六个原子体系的量子动力学计算仍很困难。为了考虑量子效应、平衡计算效率，我们选择RPMD方法。该方法基于路径积分分子动力学，通过计算通量相关函数后接着计算速率常数。引进聚环（Ring Polymer）的概念，定量地从准经典结果逼近到全量子结果。同时，精确地考虑到短时量子动力学和长时量子统计效应。

In this proposal, to compute the rate constant of the sensitive reaction H + CH4 -> H2 + CH3 in the combustion system, the recently developed ring-polymer molecular dynamics (RPMD), in conjunction with the artificial neural-network fitting potential energy surface, will be implemented. Nowadays, the deviation between the computational and experimental rate-constant values are large. It is, therefore, necessary to develope new computational method for calculating the rate constant. The RPMD method is a path-integral quantum dynamics via molecular dynamics technology. Usually, the full dimension quantum reaction dynamics with more than six atoms is impossible based on the present computor resource. By using "ring polyer", the RPMD method can accurately obtain the quantum-dynamical results via the molecular dynamics calculations in a larger phase space. The RPMD method also considers both the short-time quantum-dynamical and long-time quantum-statistical effects in computing the rate constant.