Superscalability of the random batch Ewald method

Energy stable scheme for random batch molecular dynamics

The computational bottleneck of molecular dynamics is pairwise additive long-range interactions between particles. The random batch Ewald (RBE) method provides a highly efficient and superscalable solver for long-range interactions, but the stochastic nature of this algorithm leads to unphysical self-heating effect during the simulation. We propose an energy stable scheme (ESS) for particle systems by employing a Berendsen-type energy bath. The scheme removes the notorious energy drift, which exists due to the force error even when a symplectic integrator is employed. Combining the RBE with the ESS, the new method provides a perfect solution to the computational bottleneck of molecular dynamics at the microcanonical ensemble. Numerical results for a primitive electrolyte and all-atom pure water systems demonstrate the attractive performance of the algorithm, including its dramatically high accuracy, linear complexity, and overcoming the energy drift for long-time simulations.

Hydrodynamic Effects on the Collapse Kinetics of Flexible Polyelectrolytes

Understanding the collapse kinetics of polyelectrolytes (PEs) is crucial for comprehending various biological and industrial phenomena. Despite occurring in an aqueous environment, previous computational studies have overlooked the influence of hydrodynamic interactions (HIs) facilitated by fluid motion. Here, we directly compute the Navier-Stokes equation to investigate the collapse kinetics of a highly charged flexible PE. Our findings reveal that HI accelerates PE collapse induced by hydrophobicity and multivalent salt. In the case of hydrophobicity, HI induces long-range collective motion of monomers, accelerating the coarsening of local clusters through either Brownian-coagulation-like or evaporation-condensation-like processes, depending on the strength of hydrophobicity with respect to electrostatic interaction. Regarding multivalent salt, HI does not affect the condensation dynamics of multivalent ions but facilitates quicker movement of local dipolar clusters along the PE, thereby expediting the collapse process. These results provide valuable insights into the underlying mechanisms of HI in PE collapse kinetics.

A Multiple-Fidelity Method for Accurate Simulation of MoS2 Properties Using JAX-ReaxFF and Neural Network Potentials

Reactive force field (ReaxFF) is a commonly used force field for modeling chemical reactions at the atomic level. Recently, JAX-ReaxFF, combined with automatic differentiation, has been used to efficiently parametrize ReaxFF. However, its analytical formula may lead to inaccurate predictions. While neural network-based potentials (NNPs) trained on density functional theory-labeled data offer a more accurate method, it requires a large amount of training data to be trained from scratch. To overcome these issues, we present a multiple-fidelity method that combines JAX-ReaxFF and NNP and apply the method on MoS2, a promising two-dimensional semiconductor for flexible electronics. By incorporating implicit prior physical information, ReaxFF can serve as a cost-effective way to generate pretraining data, facilitating more accurate simulations of MoS2. Moreover, in the Mo-S-H system, the pretraining strategy can reduce root-mean-square errors of energy by 20%. This approach can be extended to a wide variety of material systems, accelerating their computational research.

A random batch Ewald method for charged particles in the isothermal-isobaric ensemble

We develop an accurate, highly efficient, and scalable random batch Ewald (RBE) method to conduct molecular dynamics simulations in the isothermal-isobaric ensemble (the NPT ensemble) for charged particles in a periodic box. After discretizing the Langevin equations of motion derived using suitable Lagrangians, the RBE method builds the mini-batch strategy into the Fourier space in the Ewald summation for the pressure and forces such that the computational cost is reduced to O(N) per time step. We implement the method in the Large-scale Atomic/Molecular Massively Parallel Simulator package and report accurate simulation results for both dynamical quantities and statistics for equilibrium for typical systems including all-atom bulk water and a semi-isotropic membrane system. Numerical simulations on massive supercomputing cluster are also performed to show promising central processing unit efficiency of the RBE.

Improved Random Batch Ewald Method in Molecular Dynamics Simulations

The random batch Ewald (RBE) is an efficient and accurate method for molecular dynamics (MD) simulations of physical systems at the nano/microscale. The method shows great potential to solve the computational bottleneck of long-range interactions, motivating a necessity to accelerate short-range components of the nonbonded interactions for a further speedup of MD simulations. In this work, we present an improved RBE method for the nonbonding interactions by introducing the random batch idea to constructing neighbor lists for the treatment of both the short-range part of the Ewald splitting and the Lennard-Jones potential. The efficiency that the novel neighbor list algorithm owes to the stochastic minibatch strategy can significantly reduce the total number of neighbors. We obtain the error estimate and convergence by theoretical analysis and implement the improved RBE method in the LAMMPS package. Benchmark simulations are performed to demonstrate the accuracy and stability of the algorithm. Numerical tests on computer performance by conducting large-scaled MD simulations for systems including up to 0.1 billion water molecules are run on massive clusters with up to 50000 CPU cores, demonstrating the attractive features such as the high parallel scalability and memory-saving of the method in comparison to the existing methods.