All the computational simulation packages are limited to certain level and the problem remains unsolved by the particular package. For such issues often researchers are forced to use their own codes to solve them. There are, a wide variety of scientific programming languages, from FORTRAN to Python, can be availed for this purpose. Especially in Python, dedicated packages like PyMatGen and SciKit can be utilized to fasten the coding process.
Further, nowadays statistical methods like machine learning and artificial intelligence based approaches are widely employed to solve complicated systems or to identify a novel phenomenon.
For such purpose we currently offer, four individual courses on four major programming languages such as (1) FORTRAN (2) Python (3) Julia and (4) and the coverage of each modules are as follows:
- FORTRAN: Basic numerical programming to automate experimental calculations using large data set.
- Python: Introduction to Materials Gnome Project
- Julia: Random trees and Random forests with Julia
- R: Quantitative structure activity relationship analysis using R
For any queries or assistance please write to us.