The makefiles (inside make_scripts folder) are tested on Linux and MacOS. The quick install files do not use conda install (where solving environments can take hours), instead, it uses python -m pip. However, all 4 files create separate environments and can be run without clashing with each other. To understand more read 🐍

• Install anaconda
• Open conda prompt
  

• run chmod +x install_peslearn_quick.sh (recommended) or chmod +x install_peslearn.sh {See manual for more information}
• run ./install_peslearn_quick.sh
  

• run chmod +x install_pes2mp_quick.sh (recommended) or chmod +x install_pes2mp.sh {See manual for more information}
• run ./install_pes2mp_quick.sh

Since the ML model generating library PES-Learn uses python 3.6 separate environments are created to aviod clashes and error. After installing both files (quick or non-quick versions as it suits).

• run conda activate peslearn_quick or peslearn for the conda installed version
• run jupyter-notebook

• run conda activate pes2mp_quick or pes2mp for the conda installed version
• run jupyter-notebook

⚠️ The ML generating jupyter files (CODE 0-1) requires python 3.6 and cannot be installed with pyshtools (4D CODE 2 and 3). Use peslearn_quick environment for CODE 0-1 and pes2mp_quick environment for the rest of the codes.

• Code 0-1 are for augmenting PES data using ML package PESLearn,
• Code 2 and 3 are for fitting PES to radial coefficients and vice-versa (to calculate fitting error),
• Code 4 is a template to fit radial coefficients into molscat readable functions.

• The original code has been broken into 5 parts (Code_0-4),

• The old codes published in Supplementary Information (links to articles are provided below) can be accessed via following link.

File 2: CODE_2_PES2MP_2D.ipynb
Uses [scipy.special] for Legendre coefficient

Link: [Link to paper: See Supplementary Information]

# Bibtex citation for 2D code: multipole expansion of 2D Potential Energy Surface
@article{Kushwaha2023Jan,
	author = {Kushwaha, Apoorv and Kumar, Thogluva Janardhanan Dhilip},
	title = {{Benchmarking PES-Learn's machine learning models predicting accurate potential energy surface for quantum scattering}},
	journal = {Int. J. Quantum Chem.},
	volume = {123},
	number = {1},
	pages = {e27007},
	year = {2023},
	month = jan,
	issn = {0020-7608},
	publisher = {John Wiley {\&} Sons, Ltd},
	doi = {10.1002/qua.27007}
}

Uses [pyshtools] for calculating spherical harmonics (need separate installation: Instructions are provided in jupyter-notebook file)

File 2: CODE_2_PES2MP_4D.ipynb

Link: [Link to paper: See Supplementary Information]

# Article citation for 4D code: multipole expansion of 4D Potential Energy Surface
@article{Kushwaha2023Aug,
	author = {Kushwaha, Apoorv and Dhilip Kumar, T. J.},
	title = {{4D potential energy surface of NCCN{\textendash}H2 collision: Rotational dynamics by p-H2 and o-H2 at interstellar temperatures}},
	journal = {J. Chem. Phys.},
	volume = {159},
	number = {7},
	year = {2023},
	month = aug,
	issn = {0021-9606},
	publisher = {AIP Publishing},
	doi = {10.1063/5.0161335}
}