A Python-based Machine Learning (ML) algorithm for the calculation of Efiled around gold-silica nanoparticlers.
Developed by Mohsen Tamtaji ([email protected]) under the supervision of Professor Zhengtang Tom Luo at HKUST, Advanced Materials & Devices Laboratory
The developed ML algorithm can be used for the prediction of electric field around gild-silica nanoparticles.
Deep neural networks (DNN) is trained based on FDTD-calculated data.
The ML model is also applicable for the calculation of HOMO-LUMO energy levels and Jablonski diagram of methyene blue in the presence of electric field. The data are from TD-DFT calculations using Gaussian 09.
PyEfield needs the following pakages:
1- pickle
2- matplotlib
3- numpy
Note: There is not "pip install" of this version yet, so you need to download the ML algorithm and run the program as follows:
1- Download the TNNEfield2.pickle files into your directory
2- Download the PyEfield.py and put into your directory, open in your PyCharm, Spider, or other Python environments, define the dimention and shape in the PyEfield.py file and run the code. The program will plot the electric filed for gold-silica core-shell nanoparticles.
3- Enjoy :)
In order to calculate the HOMO-LUMO energy levels and Jablonski diagram for methylene blue photosensitizers in the presence of E (V/Å):
1- Clone the MLTDDFT file
2- Open PyTDDFTMB.py in your PyCharm, Spider, or other Python environments, define the location of your files and also E in Volt/Angstrom (V/Å). The range of E is in ± 0.271 V/Å. The program will print the HOMO, LUMO energies. It alos calculates S1, S2, T1, T2, T3, T4, T5, and ISC energies. Please see the following picture:
3- Enjoy :)
*Note: The PyTDDFTMB.py program is trained based on TD-DFT-D3 calculations using Gaussian 09 software, which can be used to calculate the excited energies of methylene blue in the presence of electric field.
For the citation, please cite the following papers:
1- DOI: 10.1039/D1TA08337F