Discrete dipole approximation codes

Discrete dipole approximation codes. This is a list of Discrete Dipole Approximation codes. The "code" here indicates computer code, a particular implementation of the DDA. For theoretical approach see Discrete dipole approximation article.
Most of the codes apply to arbitrary-shaped inhomogeneous nonmagnetic particles and particle systems in free space or homogeneous dielectric host medium. The calculated quantities typically include the Mueller matrices, integral cross-sections, internal fields and angle-resolved scattered fields. There are some published comparisons of existing DDA codes.

General-purpose open-source DDA codes

These codes typically use regular grids, conjugate gradient method to solve large systems of linear equations, and FFT-acceleration of the matrix-vector products which uses convolution theorem. Complexity of this approach is almost linear in number of dipoles for both time and memory.

Name	Authors	References	Language	Updated	Features
	Draine and Flatau		Fortran	2019	Can also handle periodic particles and efficiently calculate near fields. Uses OpenMP acceleration.
	Choliy		C++	2017	Version of DDSCAT translated to C++ with some further improvements.
	Yurkin, Hoekstra, and contributors		C	2020	Implements fast and rigorous consideration of a plane substrate, and allows rectangular-cuboid voxels for highly oblate or prolate particles. Can also calculate emission (decay-rate) enhancement of point emitters. Near-fields calculation is not very efficient. Uses Message Passing Interface parallelization and can run on GPU.
	McDonald		C	2009	Uses both OpenMP and MPI parallelization. Focuses on computational efficiency.
	Kieß		C++	2016	Runs on GPU. Algorithms are partly based on ADDA.
	Sha		C/C++	2019	Also calculates near fields and material absorption. Named differently, but the algorithms are very similar to the ones used in the mainstream DDA.
	Groth, Polimeridis, and White		Matlab	2019	Uses circulant preconditioner for accelerating iterative solvers
	Chaumet, Sentenac, and Sentenac		Fortran, GUI in C++ with Qt	2021	Idiot-friendly DDA. Uses OpenMP and HDF5. Has a separate version for multi-layered substrate.
	Shabaninezhad, Awan, and Ramakrishna		Matlab	2021	Runs on GPU
	Dibo Xu and others		Python	2025	GPU acceleration using CuPy

Specialized DDA codes

These list include codes that do not qualify for the previous section. The reasons may include the following: source code is not available, FFT acceleration is absent or reduced, the code focuses on specific applications not allowing easy calculation of standard scattering quantities.

Name	Authors	References	Language	Updated	Features
DDSURF, DDSUB, DDFILM	Schmehl, Nebeker, and Zhang		Fortran	2008	Rigorous handling of semi-infinite substrate and finite films, but only 2D FFT acceleration is used.
DDMM	Mackowski		Fortran	2002	Calculates T-matrix, which can then be used to efficiently calculate orientation-averaged scattering properties.
CDA	McMahon		Matlab	2006
	Loke		Matlab	2014	Rigorous handling of substrate, but no FFT acceleration is used.
	Dmitriev		Python	2015	Reimplementation of DDA-SI
	Vaschillo and Bigelow		Fortran	2019	Simulates electron-energy loss spectroscopy and cathodoluminescence. Built upon DDSCAT 7.1.
	Geuquet, Guillaume and Henrard		Fortran	2013	Simulates electron-energy loss spectroscopy and cathodoluminescence. Handles substrate through image approximation, but no FFT acceleration is used.
T-DDA	Edalatpour		Fortran	2015	Simulates near-field radiative heat transfer. The computational bottleneck is direct matrix inversion. Uses OpenMP and MPI parallelization.
CDDA	Rosales, Albella, González, Gutiérrez, and Moreno			2021	Applies to chiral systems
	Yibin Jiang, Abhishek Sharma and Leroy Cronin		Python	2023	Simulates nanostructures undergoing structural transformation with GPU acceleration.