The AMREEN Group
'Advanced Modelling and Research in Energy and Electrochemical Navigation'
'Advanced Modelling and Research in Energy and Electrochemical Navigation'
The AMREEN Group (Advanced Modelling and Research in Energy and Electrochemical Navigation) at the Research Institute for Sustainable Energy (RISE), TCG CREST, is dedicated to the exploration and development of next-generation energy storage materials. Our research is driven by the urgent need for efficient, safe, and sustainable battery technologies to power the future of renewable energy, electric mobility, and smart grid infrastructure. We focus on the rational design and optimization of battery components—including layered oxide cathodes, high-performance anodes, and both liquid and solid-state electrolytes—using state-of-the-art computational techniques. By leveraging tools such as Density Functional Theory (DFT), Classical and Ab Initio Molecular Dynamics (AIMD), and Machine Learning, we investigate materials at the atomic and electronic scale to understand their behavior, predict performance, and accelerate material discovery. Our interdisciplinary approach bridges fundamental physics, data-driven modeling, and advanced simulations to address key challenges in energy storage. Through collaborative innovation and rigorous scientific methodology, the AMREEN Group is committed to advancing the frontier of materials research, contributing to a cleaner and more sustainable energy future.
Principal Investigator
Computational modeling of battery materials, electrolyte design, ML for materials discovery
PhD Student
Computational design of high entropy anode materials and solid electrolyte interface analysis
PhD Student
Modeling and analysis of high-entropy cathode materials for Na-ion batteries
We study non-aqueous electrolytes and interfacial behavior to understand ion transport mechanisms and electrochemical stability across different electrodes. In this approach we use molecular dynamics (Classical as well as AIMD). Machine learning assisted MD simulations were also considered for effective analysis of CEI/SEI interfaces.
We use available ML interatomic potentials (MLIP) and perform benchmarking analysis to see which MLIPs are more efficient in our studies to accelerate materials discovery and property prediction for batteries, reducing computational costs.
We explore compositional complexity using a combination of DFT, ML, and MD simulations to identify high-entropy electrode materials with improved stability and performance.
A list of selected publications (Yearwise).
A glimpse into our group activities and moments
We are actively hiring for the following positions:
PhD Admissions are active at TCG CREST for 2025.
📢 View our official advertisement: https://www.tcgcrest.org/programs/
Deadline: 7th June 2025
🌱 Visit RISE Website – Sustainable Energy Research
At The AMREEN Group, we are looking for motivated PhD students interested in
battery materials, computational chemistry, and AI/ML in materials science.
Applicants should have a strong academic background in Chemistry, Materials Science, or a related field.
Fully funded positions are available.
📩 Contact us with your CV and a brief research statement to apply.
To be updated soon.....
To be updated soon......
