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PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, both to aid new method development, as well as for flexibility in computational workflow. The package provides a wide range of tools to support simulations of finite size systems, extended systems with periodic boundary conditions, low dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure easy of extensibility, PySCF uses the Python language to implement almost all its features, while computationally critical paths are implemented with heavily optimized C routines. Using this combined Python/C implementation, the package is as efficient as the best existing C or Fortran based quantum chemistry programs.


BLOCK implements the density matrix renormalization group (DMRG) algorithm for quantum chemistry. The DMRG is a variational wavefunction method. Compared to other quantum chemical methods, it efficiently describes strong, multi-reference correlation in a large number of active orbitals (occupancies far from 0 or 2). The method is also provably optimal for correlation with a one-dimensional topology, that is, where orbitals are arranged with a chain- or ring-like connectivity. However, with the possible exception of small molecules, for correlation that is dynamic in character, the DMRG may be less computationally efficient than other methods such as coupled cluster theory or multireference configuration interaction. We recommend the use of the DMRG in problems requiring active spaces too large for standard complete active space (CAS) techniques.

block2 is a more recent implementation of quantum chemistry DMRG using the modern matrix product operator (MPO) and matrix product state (MPS) formalism. The package supports a large variety of different extensions of DMRG, including real and imaginary time evolution, Green's function, and finite temperature approaches. The code is written in C++ with heavy parallelization and optimization for achieving production level efficiency. A python interface is available for easy integration with other methods, such as density matrix embedding theory (DMET) and dynamical mean field theory (DMFT).

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