MAVENs

Materials Advancing a Viable ENergy future

Predicting how disorder controls functional behaviour, from first principles.

Led by Dr. Rudra Banerjee, Department of Physics and Nanotechnology, SRM Institute of Science and Technology.
See our researchMeet the group
Host sites, substitutions, vacancies

Bloch’s theorem rewards a perfect crystal with clean, classifiable states — and almost no real material obliges. Substitution, vacancies, and chemical mixing are not blemishes on an ideal lattice; they are what the material actually is. Disorder is intrinsic, and it is often the thing that decides whether a material works.

We combine DFT, Monte Carlo, spin-dynamics, and machine learning — using each where the physics demands it: DFT for electronic structure, Monte Carlo and spin dynamics for finite-temperature collective behaviour, ML where compositional spaces become too large for direct first-principles study.

Research Themes

Three questions about what disorder does to a material, and the machine learning we use when there are too many compositions to compute directly.

Disorder and Magnetism How does chemical disorder decide whether a magnet orders, at what temperature, and how much magnetisation survives?
Disorder and Coherence What lets a chemically generated spin centre hold quantum phase information, and how does the disorder around it set the limit?
Disorder and Catalysis In a compositionally complex catalyst no two sites are alike — so what sets the distribution of adsorption energies, and where it sits?
Machine Learning Driven Discovery Machine-learning discovery that keeps the physical insight which makes a prediction transferable.

Research Highlights

New papers, recognition and group news.

Open Positions

We welcome M.Sc. and PhD students in physics, chemistry, or materials science. Projects span ab-initio DFT, Monte Carlo, and ML-driven screening.