i-PIΒΆ

i-PI is a universal force engine interface written in Python, designed to be used together with an ab-initio, machine-learned, or force-field based evaluation of the interactions between the atoms. You can see learn more about it on the ipi-code website, the documentation pages or the github repository.

Constant-temperature MD and thermostats
This recipe gives a practical introduction to finite-temperature molecular dynamics simulations, and provides a guide to choose the most appropriate thermostat for the simulation at hand.
Constant-temperature MD and thermostats
Path integral molecular dynamics
This example shows how to run a path integral molecular dynamics simulation using i-PI, analyze the output and visualize the trajectory in chemiscope. It uses LAMMPS as the driver to simulate the q-TIP4P/f water model.
Path integral molecular dynamics
Path integral metadynamics
This example shows how to run a free-energy sampling calculation that combines path integral molecular dynamics to model nuclear quantum effects and metadynamics to accelerate sampling of the high-free-energy regions.
Path integral metadynamics
Quantum heat capacity of water
This example shows how to estimate the heat capacity of liquid water from a path integral molecular dynamics simulation. The dynamics are run with i-PI, and LAMMPS is used as the driver to simulate the q-TIP4P/f water model.
Quantum heat capacity of water
Multiple time stepping and ring-polymer contraction
This notebook provides an introduction to multiple time stepping and ring polymer contraction, two closely-related techniques, that are geared towards reducing the cost of calculations by separating slowly-varying (and computationally-expensive) components of the potential energy from the fast-varying (and hopefully cheaper) ones.
Multiple time stepping and ring-polymer contraction
Atomistic Water Model for Molecular Dynamics
In this example, we demonstrate how to construct a metatensor atomistic model for flexible three and four-point water model, with parameters optimized for use together with quantum-nuclear-effects-aware path integral simulations (cf. Habershon et al., JCP (2009)). The model also demonstrates the use of torch-pme, a Torch library for long-range interactions, and uses the resulting model to perform demonstrative molecular dynamics simulations.
Atomistic Water Model for Molecular Dynamics
The PET-MAD universal potential
This example demonstrates how to use the PET-MAD model with ASE, i-PI and LAMMPS. PET-MAD is a "universal" machine-learning forcefield trained on a dataset that aims to incorporate a very high degree of structural diversity.
The PET-MAD universal potential