Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles
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DOI：
10.1177/1094342019899457
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发表时间：
2019-10
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影响因子：
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通讯作者：
J. Kadupitiya;G. Fox;V. Jadhao
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文献类型：
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作者：
J. Kadupitiya;G. Fox;V. Jadhao
使用粗粒度模型模拟可极化纳米颗粒（NPs）附近离子的动力学是极具挑战性的，因为需要在每个模拟时间步解泊松方程。最近，一种基于动态优化框架的分子动力学方法绕过了这一障碍，该方法将极化电荷密度表示为虚拟动态变量，并将其与离子的物理动力学并行发展。通过展示如何在极化NPs附近离子的MD模拟中实现机器学习（ML）方法的参数预测，我们强调了在MD模拟中集成机器学习（ML）方法可获得的计算增益。将基于人工神经网络的回归模型与MD仿真相结合，预测出表征虚拟系统的最优仿真时间步和优化参数，成功率为94.3%。机器学习支持的参数自动调谐产生了大约1000万步的离子精确动态，同时将模拟的稳定性提高了一个数量级以上。将机器学习增强框架与混合开放多处理/消息传递接口（OpenMP/MPI）并行化技术相结合，将具有数千个离子和感应电荷的模拟系统的计算时间从数千小时减少到数十小时，仅机器学习加速的最大加速速度≈3，机器学习和并行计算方法相结合的最大加速速度≈600。在油水乳液附近的浓电解质中离子结构的提取证明了该方法的成功。该方法可推广到其他MD应用和能量最小化问题的最优参数选择。
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.