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Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
The International Journal of High Performance Computing Applications ( IF 3.1 ) Pub Date : 2020-01-14 , DOI: 10.1177/1094342019899457
JCS Kadupitiya , Geoffrey C Fox , Vikram Jadhao 1
Affiliation  

Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network–based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid Open Multi-Processing / Message Passing Interface (OpenMP/MPI) parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil–water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.

中文翻译:

用于分子动力学模拟中参数自动调整的机器学习:可极化纳米粒子附近离子的有效动力学

由于需要在每个模拟时间步长求解泊松方程,因此使用粗粒度模型模拟可极化纳米粒子 (NP) 附近的离子动力学极具挑战性。最近,一种基于动态优化框架的分子动力学 (MD) 方法通过将极化电荷密度表示为虚拟动态变量并将它们与离子的物理动力学并行发展,绕过了这一障碍。我们通过展示如何在可极化 NP 附近的离子 MD 模拟中实现机器学习 (ML) 方法在 MD 模拟中进行参数预测的集成,强调了计算增益。基于人工神经网络的回归模型与 MD 模拟相结合,并以 94.3% 的成功率预测了表征虚拟系统的最佳模拟时间步长和优化参数。支持 ML 的参数自动调整产生了大约 1000 万步的精确离子动态,同时将模拟的稳定性提高了一个数量级。ML 增强框架与混合开放式多处理/消息传递接口 (OpenMP/MPI) 并行化技术的集成将模拟具有数千个离子和感应电荷的系统的计算时间从数千小时减少到数十小时,从而产生最大来自仅 ML 加速的 ≈ 3 加速,以及来自 ML 和并行计算方法组合的最大加速 ≈ 600。在油水乳液附近的浓缩电解质中提取离子结构证明了该方法的成功。该方法可以推广到在其他 MD 应用和能量最小化问题中选择最佳参数。
更新日期:2020-01-14
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