Wu Stephen, Angelikopoulos Panagiotis, Tauriello Gerardo, Papadimitriou Costas, Koumoutsakos Petros
Computational Science and Engineering Laboratory, ETH-Zurich, Clausiusstrasse 33, CH-8092 Zurich, Switzerland.
Department of Mechanical Engineering, University of Thessaly, 38334 Volos, Greece.
J Chem Phys. 2016 Dec 28;145(24):244112. doi: 10.1063/1.4967956.
We propose a hierarchical Bayesian framework to systematically integrate heterogeneous data for the calibration of force fields in Molecular Dynamics (MD) simulations. Our approach enables the fusion of diverse experimental data sets of the physico-chemical properties of a system at different thermodynamic conditions. We demonstrate the value of this framework for the robust calibration of MD force-fields for water using experimental data of its diffusivity, radial distribution function, and density. In order to address the high computational cost associated with the hierarchical Bayesian models, we develop a novel surrogate model based on the empirical interpolation method. Further computational savings are achieved by implementing a highly parallel transitional Markov chain Monte Carlo technique. The present method bypasses possible subjective weightings of the experimental data in identifying MD force-field parameters.
我们提出了一种分层贝叶斯框架，用于系统地整合异构数据，以校准分子动力学（MD）模拟中的力场。我们的方法能够融合系统在不同热力学条件下的各种物理化学性质的实验数据集。我们利用水的扩散率、径向分布函数和密度的实验数据，证明了该框架对MD力场进行稳健校准的价值。为了解决与分层贝叶斯模型相关的高计算成本问题，我们基于经验插值方法开发了一种新型替代模型。通过实施高度并行的过渡马尔可夫链蒙特卡罗技术，进一步节省了计算量。本方法在识别MD力场参数时绕过了实验数据可能的主观加权。
