Defining, calculating and converging observables of kinetic transition networks.
Defining, calculating and converging observables of kinetic transition networks.
复制标题
定义、计算和收敛动力学转变网络的可观测量。
DOI：
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发表时间：
2020
影响因子：
5.5
通讯作者：
D. Wales
中科院分区：
文献类型：
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作者：
T. Swinburne;D. Wales
局部极小值和过渡态的动力学跃迁网络能够捕捉化学、生物学和材料科学中许多系统的动力学。然而，对于大型网络，提取可观测量在数值上具有挑战性，并且通常对额外的计算发现敏感。为了对可观测量进行任何收敛性测量，必须定期计算这些灵敏度。我们提出了一个矩阵制定的动力学过渡网络的离散路径采样框架，推导出分支概率，过渡率和等待时间的表达式。利用准平稳分布的概念，建立了一个清晰的网络观测值的表达层次，从精确的结果到稳态近似。我们使用这些结果与图形变换方法相结合，推导出相对于已知的动力学过渡网络的扰动的灵敏度，给出明确的条款成对的灵敏度，并讨论了路径的灵敏度。这些结果提供了指导方针收敛网络相对于额外的采样，专注于产品和反应物状态之间的整体速率系数获得的估计。我们证明了这一过程中的双漏斗景观的38个原子的Lennard-Jones集群的过渡。
Kinetic transition networks of local minima and transition states are able to capture the dynamics of numerous systems in chemistry, biology and materials science. However, extracting observables is numerically challenging for large networks and will in general be sensitive to additional computational discovery. To have any measure of convergence for observables, these sensitivities must be regularly calculated. We present a matrix formulation of the discrete path sampling framework for kinetic transition networks, deriving expressions for branching probabilities, transition rates, and waiting times. Using the concept of the quasistationary distribution a clear hierarchy of expressions for network observables is established, from exact results to steady state approximations. We use these results in combination with the graph transformation method to derive the sensitivity with respect to perturbations of the known kinetic transition network, giving explicit terms for the pairwise sensitivity, and discussing the pathwise sensitivity. These results provide guidelines for converging the network with respect to additional sampling, focusing on the estimates obtained for the overall rate coefficients between product and reactant states. We demonstrate this procedure for transitions in the double-funnel landscape of the 38 atom Lennard-Jones cluster.
影响因子：
4.4
作者：
Faradjian, AK;Elber, R
通讯作者：
Elber, R