Computational Science and Engineering Laboratory, ETH Zürich, CH-8092 Zurich, Switzerland.
J Chem Phys. 2012 Oct 14;137(14):144103. doi: 10.1063/1.4757266.
We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon.
我们提出了一个贝叶斯概率框架，用于量化和传播分子动力学（MD）模拟中所使用力场参数的不确定性。我们提出了一种高效的过渡马尔可夫链蒙特卡罗方法的实现，用于填充 MD 力场参数的后验概率分布。我们提出了有效的调度算法来处理 MD 模型运行，并在具有异构架构的集群中分配计算。此外，还提出了自适应替代模型，以降低与大量 MD 模型运行相关的计算成本。在所提出的贝叶斯框架的 MD 模拟液体和气体氩中证明了其有效性和计算效率。
