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acorn's Introduction

Quantum Acorn


Quantum Acorn, a collection of electronic structure methods compiled into a dependency-free binaries. If you are here for the educational scripts, you can find them in the education folder.

Features

Below are all the important features of Acorn divided into categories. If you are looking for the educational scripts, you can find them in the education folder.

Quantum Mechanical Methods

  • Numerically Exact Adiabatic & Nonadiabatic Quantum Dynamics
  • Hartree-Fock Method & Møller–Plesset Perturbation Theory
  • Configuration Interaction & Coupled Clusters Methods

Compilation

The software requires the fftw, libint and pytorch libraries. Before compiling these libraries and proceeding with the following steps, make sure you have eigen, boost and openblas libraries installed. On debian-based distributions, you can do it with the following command.

sudo apt install libboost-dev libeigen3-dev libopenblas-dev

To compile the libraries execute ./script/libfftw.sh && ./script/libint.sh && ./script/libtorch.sh from the project root directory. After this, the project configuration should finish without errors.

cmake -B build -DCMAKE_BUILD_TYPE=Release

And we can build with the following command.

cmake --build build

After the compilation the bin folder will be created along with the executables.

Examples

All the examples are implemented as example/makefile targets. If you are in example directory, you can run the HF example on a water molecule simply run the make hf command. The calculation should finish without errors. Feel free to explore all the examples.

Credits

  • argparse - Argument Parser for Modern C++.
  • eigen - C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
  • exprtk - C++ Mathematical Expression Parsing and Evaluation Library.
  • fftw - C Subroutine Library for Computing the Discrete Fourier Transform.
  • libint - High-Performance Library for Computing Gaussian Integrals in Quantum Mechanics.
  • pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.

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