Research

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 is rooted in a simple question: How can we computationally design materials that power a sustainable and quantum-enabled future?

From the quantum spin textures of disordered Heusler alloys to the catalytic activity of doped MXenes, our work bridges fundamental theory with high-impact applications. We explore the structure-property relationships of complex systems using tools from density functional theory (DFT), statistical mechanics, and AI-driven discovery.

Discover more about the paths we take to achieve our goal below:

Disordered Materials
Disordered Materials

Disordered systems stand apart due to their lack of long-range atomic order, which often enhances their mechanical, magnetic, and electronic properties. In HEAs, a multi-component, near-equimolar composition creates high configurational entropy, resulting in properties like exceptional strength, corrosion resistance, and thermal stability.

Materials Informatics
Materials Informatics

The discovery of novel functional materials underpins the modern technological revolution, yet identifying materials with specific properties within an immense chemical composition space remains a formidable challenge. The complex structure-property relationships in materials make this search inherently difficult.

Single Magnetic Molecules
Single Magnetic Molecules

Single magnetic molecules, such as organometallic systems, and 2D materials offer immense potential as molecular magnets and qubits for quantum information processing and spintronic devices. Their tunable electronic states and intrinsic magnetic properties make them ideal candidates for such applications.