i-PI: A Python interface for ab initio path integral molecular dynamics simulations

By Michele Ceriotti, Joshua More, David E. Manolopoulos. 2014

260 citations recorded by the source corpus.

Recent developments in path integral methodology have signicantly reduced the computational expense of including quantum mechanical eects in the nuclear motion in ab initio molecular dynamics simulations. However, the implementation of these developments requires a considerable programming eort, which has hindered their adoption. Here we describe i-PI, an interface written in Python that has been designed to minimise the eort required to bring state-of-the-art path integral techniques to an electronic structure program. While it is best suited to rst principles calculations and path integral molecular dynamics, i-PI can also be used to perform classical molecular dynamics simulations, and can just as easily be interfaced with an empirical forceeld code. To give just one example of the many potential applications of the interface, we use it in conjunction with the CP2K electronic structure package to showcase the importance of nuclear quantum eects in high pressure water.

Subfields: Atomic and Molecular Physics, and Optics, Geophysics

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In Atomic and Molecular Physics, and Optics

1. Linfeng Zhang
2. Han Wang
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In Geophysics

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Citations in the corpus

• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (2017, background): “Considering the potential impact that deep learning-based methods will have on molecular simulation, it is of considerable interest to develop open source platforms that serve as the interface between deep neural network models and molecular simulation tools such as LAMMPS [19], Gromacs [20] and NAM”
• Deep Potentials for Materials Science (2022, methodology): “…simulation tasks such as MD, Monte Carlo, geometric optimisation, ...by interfacing DeePMD-kit with molecular simulation packages, such as the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [81], the Atomic Simulation Environment (ASE) [82], i-PI [83], and GRO-MACS [84].”