At MAVENs, we harness first-principles modeling, Monte Carlo simulations, and machine learning to unlock the properties of materials for next-generation technologies in energy, magnetism, and quantum applications.

Our Research Philosophy

Our research is rooted in a fundamental question:

How can we computationally design materials that power a sustainable and quantum-enabled future?

We address this question by bridging fundamental theory with high-impact applications—ranging from the catalytic activity of doped MXenes and the quantum spin textures of disordered Heusler alloys to the classification of NV-defect host sites using machine learning. By uncovering structure–property relationships in complex materials, we harness tools such as density functional theory (DFT), the Monte Carlo method, and AI-driven discovery to guide the design of next-generation quantum and energy materials.

These efforts are enabled by a suite of cutting-edge computational techniques, forming the foundation of our research workflow.

Computational Methods & Tools

Our research employs state-of-the-art computational techniques:

• Density Functional Theory (DFT): For accurate prediction of electronic and structural properties
• Monte Carlo Simulations: For statistical sampling of complex configurational spaces
• Machine Learning: For pattern recognition and property prediction
• High-throughput Computing: For systematic materials screening

Together, these methods allow us to explore and predict the behaviour of materials across length scales and functional regimes.

Collaborative Research

We believe the most impactful research happens at the intersection of theory and experiment. Our group actively collaborates with experimental research teams worldwide, ensuring that our computational predictions are both validated and translated into real-world applications.

These partnerships enhance the practical relevance of our work and accelerate the deployment of advanced materials.

Research Impact

Our computational insights drive materials innovation by:

• Reducing experimental trial-and-error
• Predicting novel materials before synthesis
• Understanding fundamental mechanisms
• Accelerating technology development timelines

Our work contributes directly to global efforts in clean energy, quantum information science, and advanced manufacturing.

Research Themes

Our research spans multiple interconnected themes. Explore them below to see how our philosophy and methods are applied to specific material systems and scientific challenges.