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Micellization Studied by GPU-Accelerated Coarse-Grained Molecular Dynamics.

Benjamin G Levine¹, David N LeBard¹, Russell DeVane²

¹Institute for Computational Molecular Science and Department of Chemistry, Temple University , Philadelphia, Pennsylvania 19122, United States.

Journal of Chemical Theory and Computation

|November 25, 2015

Summary

This summary is machine-generated.

Computational materials design using surfactant self-assembly is advanced by combining coarse-grained modeling with GPU acceleration. This approach bridges simulation and laboratory time scales, enabling faster and more efficient materials discovery.

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Area of Science:

Materials Science
Computational Chemistry
Chemical Engineering

Background:

Computational design of advanced materials via surfactant self-assembly faces challenges due to limited simulation scales.
Bridging laboratory and computational time scales is crucial for accurate materials prediction.

Purpose of the Study:

To implement a coarse-grained (CG) force field into GPU-accelerated molecular dynamics (MD) software.
To enable simulations of surfactant self-assembly at unprecedented scales.
To investigate the properties of polyethylene glycol (PEG) surfactants in aqueous solutions.

Main Methods:

Implemented a previously reported CG force field into the HOOMD-Blue GPU-accelerated MD software.
Utilized a single GPU for simulations, achieving superior performance over CPU-based clusters.
Collected 0.6 ms of MD trajectory data for 7 nonionic PEG surfactant systems.

Main Results:

Achieved high-performance simulations on a single GPU, outperforming traditional CPU clusters.
Calculated critical micelle concentrations (CMCs) in good agreement with experimental data.
Characterized micelle size and shape, observing continued growth in hydrophobic surfactant systems.

Conclusions:

The combination of CG modeling and GPU acceleration significantly advances the computational prediction of surfactant system properties.
Microsecond-scale simulations provide valuable insights but may require longer durations or advanced sampling for equilibrium distributions.
This work represents a substantial step towards the in silico design of advanced materials through surfactant self-assembly.