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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.
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Ecological Impact of Green Computing Using Graphical Processing Units in Molecular Dynamics Simulations
Molecular dynamics (MD) models require comprehensive computational power to simulate nanoscale phenomena. Traditionally, central processing unit (CPU) clusters have been the standard method of performing these numerically intensive computations. This article investigates the use of graphical processing units (GPUs) to implement large-scale MD models for exploring nanofluidic-substrate interactions. MD models of water nanodroplets over flat silicon substrate are tracked wherein the simulation attains a steady state computational performance. Different classes of GPU units from NVIDIA (C2050, K20, and K40) are evaluated for energy efficiency performance with respect to three green computing measures: simulation completion time, power consumption, and CO2 emissions. The CPU+K40 configuration displayed the lowest energy consumption profile for all the measures. This research demonstrates the use of energy efficient graphical computing versus traditional CPU computing for high-performance molecular dynamics simulations.
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- Award ID(s):
- 1663128
- PAR ID:
- 10119504
- Date Published:
- Journal Name:
- International Journal of Green Computing
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 1948-5018
- Page Range / eLocation ID:
- 35 to 48
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.4018/IJGC.2018010103
