cMF_data

This directory accumulates the examples of cMF calculation based on Mayhall group's cMF link implemenattaion. Perform CMF (Cluster Mean-Field) calculations. This is simply a variational optimization of both orbital and cluster state parameters, minimizing the energy of a single TPS. This was originally proposed by Scuseria and coworkers link.

Installation with Conda

1. Download

   git clone https://github.com/nmayhall-vt/ClusterMeanField.jl.git
   cd ClusterMeanField.jl/

2. Create conda environment to install Julia and will hold the PySCF executable. Install Julia with conda makes sure the correct python version will be found when using PyCall. where -tauto let's Julia pick the max number of threads. Use -t N to select N manually. Removing defaults to 1 thread.

   conda create -n my_env
   conda activate my_env
   conda install python==3.7
   conda config --add channels conda-forge
   conda install -c pyscf pyscf
   conda install h5py==2.10.0
   conda install julia
   julia --project=./ -tauto  

3. Build PyCall from Julia REPL

   using Pkg; Pkg.build("PyCall")

4. Run examples

   include("./filepath")
   

Installation with Virtual Environment

(might have issues with building PyCall and directing to correct python version)

1. Download

   git clone [email protected]:nmayhall-vt/ClusterMeanField.jl.git
   cd ClusterMeanField.jl/

2. Create python virtual environment which will hold the PYSCF executable. where -tauto let's Julia pick the max number of threads. Use -t N to select N manually. Removing defaults to 1 thread.

   virtualenv -p python3 venv
   source venv/bin/activate
   pip install -r requirements.txt
   export PYTHON=$(which python)
   julia --project=./ -tauto 

3. Build PyCall from Julia REPL

   using Pkg; Pkg.build("PyCall")

4. Run examples

   include("./filepath")