MAVENs

Materials Advancing a Viable ENergy future

Governing Quantum Materials

When does local electronic structure decide a material’s function, and when does something larger take over? We track how symmetry breaking — a vacancy, a Janus stacking, a substitutional site — propagates from local descriptor to collective spin-dependent response.
doi: 10.1016/j.jssc.2024.124602
doi: 10.1063/5.0238199
doi: 10.1016/j.jpcs.2024.111914

Energy Materials by Design

Which features of the electronic structure actually decide whether a material solves an energy problem? We isolate electronic-structure control variables — d-band filling, site-resolved d-band centre, Fermi-level position — to make compositional design predictive rather than exploratory.
doi: 10.1016/j.physb.2025.417148

You may say we are dreamers
— John Lennon, Imagine (slightly modified)

MAVENs (Materials Advancing a Viable ENergy Future), led by Dr. Rudra Banerjee, is based in the Department of Physics and Nanotechnology at the SRM Institute of Science and Technology.

We address a central question in computational materials science:

How do we computationally design materials that solve energy and quantum challenges?

Our approach combines Density Functional Theory (DFT), Monte Carlo simulations, and Machine Learning (ML) to predict material properties from electronic structure—bridging atomic-scale behaviour with macroscopic functionality. By linking fundamental theory with experimentally relevant outcomes, we predict material behaviour before synthesis, reducing trial-and-error and accelerating discovery.

Research Themes

Energy Materials by Design Which electronic-structure variables actually control catalytic, magnetic, and electronic function in itinerant systems?

Quantum Materials by Design How do local electronic features — at a vacancy, a Janus interface, a substitutional site — propagate into collective spin-dependent responses?

Interpretable Machine Learning Physics-informed ML that discovers materials while preserving the mechanistic insight that makes predictions transferable.

Explore Our Research

Research Highlights

May 2026 New Paper in PRB: Half-metallicity in 2D TMDCs

May 2026 New Paper in PRM: Machine Learning for Quantum Defect Hosts

May 2026 IonVis Wins QtHack

Nov 2025 Karunakaran defended his thesis

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.

See Open Projects