Hydrogen storage in materials: Analysis using density functional theory and artificial intelligence

Theoretical Background of the Program

The Hydrogen Storage in Materials program, "Density Functional Theory and Artificial Intelligence Analysis," addresses the scientific and technological challenges of developing efficient and safe hydrogen storage solutions, recognizing hydrogen as a crucial clean energy carrier for the future. Understanding the atomic and electronic interactions between hydrogen and various materials is essential for designing materials with high efficiency in absorption, storage, and release.

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

General Objective of the Training Program

1. To enable participants to understand the scientific principles of hydrogen storage in materials at the atomic and electronic levels.

2. To provide trainees with practical knowledge of the Density Functional Theory (DFT) methodology for analyzing and modeling materials.

3. To employ artificial intelligence and machine learning techniques in predicting the properties of hydrogen storage materials.

4. To enhance the ability to link theoretical modeling with experimental results in materials science research.

5. To develop data analysis and computer simulation skills in the field of energy and hydrogen.

6. To enable participants to design and study new, highly efficient hydrogen storage materials.

7. To support the research, development, and innovation capabilities of researchers and engineers in clean energy technologies.

Main Topics of the Training Program

Target Audience for the Training Program