Machine Learning-based pedictive tool for energy storage

Overview

The fundamental idea behind the ML approach is to analyze and map the relationships between the physical,chemical, and energy storage properties of materials with their associated output data. This early understanding of the energy storage capabilities through the ML approach helps the material scientists to clearly understand, discover, and optimize the fabrication process to develop highly efficient energy storage systems. It also provides key steps in the device fabrication process omitting excessive experimental stages.

Research activities

The research activities can include:

• Development of algorithm
• Data collection and analysis
• Programming

The supervisor has a strong track record and a wide range of expertise in materials for energy storage technologies. This is symbiotic opportunity to work with research team in materials and data science field.

Outcomes

This project connects Data Science with Material Science and will develop an advanced predictive tool to develop materials for energy storage systems such as batteries and supercapacitors.

The best results of this research are expected to be published in high impact journals, and presented in the national and international conferences.

Skills and experience

The candidate is expected to have a strong background in:

• Data science - Machine Learning
• Programming (python)
• physics
• Material science

Scholarships

You may be eligible to apply for a research scholarship.

Explore our research scholarships

Keywords

• Machine learning
• Materials
• Energy storage

Contact

Contact the supervisor for more information.