Beyond harmonic transition state theory for accelerating molecular dynamics

德克萨斯大学奥斯汀分校的Graeme Henkelman在开发加速分子动力学模拟的算法方面得到了化学理论、模型和计算方法(CTMC)计划和网络基础设施办公室(OCI)的支持。对于稀有事件系统，如固体中的扩散和表面上的反应，过渡态理论(TST)允许将分子振动和感兴趣的反应事件之间的时间尺度分开。当反应机理未知时，采用一种自适应的动力学蒙特卡罗(KMC)方法寻找鞍点，并用调和TST估计反应速率。为了降低计算成本，采用了一个数据库来存储动力学事件，并使它们可用于以后进行类似化学成分的单独计算。当一类系统的反应机理已知时，计算成本较低，因为所有事件都从数据库中提取。为了进一步加速模拟，使用主方程的解析解来避开由快速率连接的状态组。存储这些状态的本地连接对于提高效率至关重要。精度将通过一系列方法来提高，范围从调和TST到(经典的)精确的完整TST加上动态校正。TST的挑战是找到一个将反应物与任何产物状态分开的分割面。正在开发支持向量机，以提供基于学习数据的分析分类功能。决策面周围的采样点将被用来训练机器，使其能够在不预先知道反应机理的情况下提供准确的过渡态。这项工作旨在缓解模拟真实的化学或材料过程所需的超长计算时间，例如催化、蛋白质折叠、分子扩散等。基本分子运动的特征时间比我们在实验室中测量的时间尺度快很多很多倍，因此必须开发加速算法。EON2是由PI和合作者开发的一个分布式开源程序，用于计算系统中的长时间尺度分子动力学，例如在金属基质上进行催化和聚集。它将使用先进的数据库技术和机器学习，并将通过定制的界面与其他软件一起使用。PI为任何有关于算法、软件或科学的问题的人举办了一个论坛。该项目已经开放，以便校园内的计算机、有空闲时间的超级计算机以及公众中的任何人都可以贡献计算资源来加速原子级别的动力学模拟。外展计划的这一方面使公众能够直接接触到作为该项目的一部分进行的研究。

Graeme Henkelman from the University of Texas at Austin is supported by the Chemical Theory, Models and Computational Methods (CTMC) Program and the Office of Cyber Infrastructure (OCI) in developing algorithms for accelerating molecular dynamics simulations. For rare event systems, such as diffusion in solids and reactions at surfaces, transition state theory (TST) allows for a separation of time scales between that of molecular vibrations and the reactive events of interest. When reactive mechanisms are not known, an adaptive version of the kinetic Monte Carlo (KMC) method is used to find saddle points and rates are evaluated with harmonic TST. To reduce the computational cost, a database is employed to store kinetic events and to make them available for later use in separate calculations with similar chemistry. When the reaction mechanisms for a class of system are known, the computational cost is low because all events are drawn from the database. To further accelerate the simulations, groups of states connected by fast rates are escaped using an analytic solution to the master equation. Storing the local connectivity of these states is essential for efficiency. Accuracy will be improved with a set of methods ranging from harmonic TST to the (classically) exact full TST plus dynamical corrections. The challenge of TST is finding a dividing surface that separates reactants from any product state. A support vector machine is being developed to provide an analytic classification function based upon learned data. Sampled points around the decision surface will be used to train the machine so that it can provide an accurate transition state without prior knowledge of reaction mechanisms.This work is directed at alleviating the ultralong computational times needed for simulation of realistic chemical or material processes, e.g., catalysis, protein folding, molecular diffusion and so on. The characteristic times for basic molecular motion are many, many times faster than the time scales we measure in the laboratory, and so acceleration algorithms must be developed. EON2 is being developed as a distributed open-source program developed by the PI and collaborators to calculate long-timescale molecular dynamics in systems, for example, undergoing catalys and clustering on metal substrates. It will use advanced database techniques and machine learning, and will be usable in conjunction with other software through customized interfaces. A discussion forum is hosted by the PI for anyone with questions about the algorithms, software or science. The project has been opened up so that computers on campus, supercomputers with idle time, and anyone from the public can contribute computational resources to accelerate dynamics simulations at the atomic scale. This aspect of the outreach program gives the public direct access to the research being done as part of this project.