fix pimd/langevin: An Efficient Implementation of Path Integral Molecular Dynamics in LAMMPS

TL;DR

This paper introduces fix pimd/langevin, an efficient PIMD implementation in LAMMPS that leverages MPI for high parallel performance, enabling faster simulations of quantum effects in molecular systems, validated with water benchmarks.

Contribution

The paper presents a new, highly efficient PIMD implementation in LAMMPS that significantly accelerates quantum molecular dynamics simulations using modern parallel computing techniques.

Findings

Achieves several-fold speedup over i-PI in water simulations.

Demonstrates strong and weak parallel scaling performance.

Supports common PIMD features within LAMMPS.

Abstract

Path integral molecular dynamics (PIMD), which maps a quantum particle onto a fictitious classical system of ring polymers and propagates the "beads" of this extended classical system using molecular dynamics, is widely used to capture nuclear quantum effects (NQEs) in molecular simulations. Accurate PIMD calculations typically require a large number of beads and are therefore computationally demanding. While software packages such as i-PI offer comprehensive PIMD functionality, the high efficiency of simulations driven by machine learning interatomic potentials, such as Deep Potential (DP), calls for more efficient PIMD implementations that fully exploit modern massively parallel supercomputers. Here we present fix pimd/langevin, an efficient PIMD implementation in LAMMPS that supports commonly used features and leverages the Message Passing Interface architecture of LAMMPS to achieve high computational efficiency. We demonstrate the usage and validate the correctness of our code using liquid water as a representative example, and provide a comprehensive overview of the supported features. Then we discuss several important technical aspects of the implementation. Using DP simulations of water as a benchmark, we show that our implementation achieves several-fold acceleration compared to i-PI. Finally, we report strong and weak scaling results that demonstrate the favorable parallel performance of our code.