|
1 | | -# Multipole expansion |
| 1 | +# PES2MP (Potential Energy Surface Mapping to Multipole Expansion Series) |
| 2 | +## Multipole expansion |
2 | 3 | 2D and 4D multipole expansion code (using Legendre polynomials and Spherical Harmonics respectively) |
3 | | -for fitting PES into radial coefficients is provided as jupyter-notebook files. |
| 4 | +for fitting PES into radial coefficients is provided as jupyter-notebook files. <br /> |
4 | 5 |
|
5 | | -Both codes use least square fit (achieved by taking pseudo-inverse of Legendre/Spherical-Harmonics coefficients stored in a 2D matrix). |
| 6 | +Both codes use least square fit (achieved by taking the pseudo-inverse of Legendre/Spherical-Harmonics coefficients stored in a 2D matrix).<br /> |
| 7 | +Currently limited to rigid rotor - atom (2D) and rigid rotor - rigid rotor (4D) collision |
6 | 8 |
|
7 | | -For any quaries contact [Dr. T. J. Dhilip Kumar](mailto:dhilip@iitrpr.ac.in) CC: AK(mailto:kushwaha.apoorv@gmail.com) |
| 9 | +For any queries contact [Dr. T. J. Dhilip Kumar](mailto:dhilip@iitrpr.ac.in) cc: (mailto:kushwaha.apoorv@gmail.com)<br /> |
8 | 10 |
|
9 | | -File 1: 2D_multipole_inv.ipynb |
10 | | -2D PES (Atom - Rigid Rotor collision) |
11 | | - |
| 11 | +### File 1: 2D_multipole_inv.ipynb |
| 12 | +_Uses scipy.special for Legendre coefficient_ |
12 | 13 |
|
13 | | -# Citation |
14 | | ---- |
15 | | -output: |
16 | | - md_document: |
17 | | - variant: markdown_github |
18 | | -bibliography: bibliography.bib |
19 | | ---- |
| 14 | +2D PES (Atom - Rigid Rotor collision)<br /> |
| 15 | +<img src="https://github.com/apoorv-kushwaha/Multipole/blob/main/jacobi22.png" width="250"> |
20 | 16 |
|
21 | | -[@benchPES] |
22 | 17 |
|
23 | | -File 2: Use 4D_SF_expansion.ipynb |
24 | | -4D PES (Two Rigid Rotors) |
25 | | - |
| 18 | +```diff |
| 19 | +# Citation: multipole expansion of 2D Potential Energy Surface |
| 20 | +@article{Kushwaha2023Jan, |
| 21 | + author = {Kushwaha, Apoorv and Kumar, Thogluva Janardhanan Dhilip}, |
| 22 | + title = {{Benchmarking PES-Learn's machine learning models predicting accurate potential energy surface for quantum scattering}}, |
| 23 | + journal = {Int. J. Quantum Chem.}, |
| 24 | + volume = {123}, |
| 25 | + number = {1}, |
| 26 | + pages = {e27007}, |
| 27 | + year = {2023}, |
| 28 | + month = jan, |
| 29 | + issn = {0020-7608}, |
| 30 | + publisher = {John Wiley {\&} Sons, Ltd}, |
| 31 | + doi = {10.1002/qua.27007} |
| 32 | +} |
| 33 | +``` |
| 34 | + |
| 35 | +### File 2: Use 4D_SF_expansion.ipynb |
| 36 | + |
| 37 | +_Uses pyshtools for calculating spherical harmonics_ |
| 38 | +**(need separate installation: Instructions are provided in jupyter-notebook file)<br />** |
| 39 | + |
| 40 | +4D PES (Two Rigid Rotors)<br /> |
| 41 | +<img src="https://github.com/apoorv-kushwaha/Multipole/blob/main/jac_final.png" width="500"> |
| 42 | + |
| 43 | + |
| 44 | +```diff |
| 45 | +! bibliography: Upcoming.bib |
| 46 | +``` |
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