Hydrogen Storage in Materials: Analysis Using Density Functional Theory and Artificial Intelligence

Theoretical Background of the Program

The program “Hydrogen Storage in Materials: Analysis Using Density Functional Theory and Artificial Intelligence” addresses the scientific and technical challenges associated with developing effective and safe solutions for hydrogen storage, as it is one of the most important carriers of clean energy for the future. The program aims to understand the atomic and electronic interactions between hydrogen and various materials, and to design materials with high efficiency in absorption, storage, and release.

The program is based on Density Functional Theory (DFT) as an advanced scientific framework for analyzing the electronic and structural properties of materials at the atomic level, while integrating artificial intelligence and machine learning techniques to accelerate modeling processes, predict material behavior, and discover new and promising structures without relying solely on costly and time-consuming experiments.

General Objective of the Training Program

1. Enable participants to understand the scientific principles of hydrogen storage in materials at the atomic and electronic levels.
2. Provide trainees with practical knowledge of the Density Functional Theory (DFT) methodology for analyzing and modeling materials.
3. Utilize artificial intelligence and machine learning techniques to predict the properties of hydrogen storage materials.
4. Enhance the ability to link theoretical modeling with experimental results in materials science research.
5. Develop skills in data analysis and computational simulation in the field of energy and hydrogen.
6.Enable participants to design and study new high-efficiency materials for hydrogen storage.
7. Support the capabilities of researchers and engineers in research, development, and innovation in clean energy technologies.

Main Topics of the Training Program

Target Audience for the Training Program