Smilei is a collaborative project providing physicists with an open-source, user-friendly, high-performance and multi-purpose electromagnetic Particle-In-Cell (PIC) code for plasma simulation.

The code is developed in C++ based on an object-oriented architecture. To face the diverse needs of the Smilei community, it offers modularity:

  • various geometries (Cartesian 1D, 2D, 3D or cylindrical with decomposition into azimuthal modes),

  • arbitrary laser or plasma profiles (any Python function),

  • various Maxwell solvers, particle pushers, interpolators, projectors

  • an envelope solver, including in the cylindrical geometry

  • advanced boundary conditions (e.g. Perfectly-Matched Layers)

  • etc.

The user-friendly interface consists in input files written in the Python language, and a whole set of run-time diagnostics (outputs in HDF5) and user-friendly (Python) post-processing tools complement the code.

Co-developed by HPC specialists and physicists, Smilei is designed for high performances on massively-parallel super-computers. It benefits from a state-of-the-art hybrid MPI/OpenMP parallelization, dynamic load balancing and SIMD vectorization. It has been successfully tested on various architectures, among which the most recent Intel Cascadelake (CSL) & Fujitsu A64FX (ARM).

In Smilei, Maxwell’s equations are solved using a Yee mesh, where the electric and magnetic fields are centered following the finite-difference time-domain (FDTD) method or related methods. A pseudo-spectral analytical time domain method is also available as an experimental feature. Charge deposition follows a charge-conservation scheme.

As a multi-purpose code, Smilei is applied to a wide range of physics-related studies: from relativistic laser-plasma interaction to astrophysics. Smilei thus benefits from various additional physics modules, among which field ionization, binary collisions and impact ionization. QED processes, such as high-energy photon emission and its back-reaction on the electron dynamics, as well as pair production through the Breit-Wheeler process, are also included.

An initial detailed account (as of 2018) of Smilei’s capabilities is given in this article. For publications on more advanced features, please refer to the Publications section of this documentation.