Research

A perfect crystal is easy to compute. Bloch’s theorem turns 10²³ atoms into one unit cell and a set of sharp states.

No real material is a perfect crystal. At MAVENs, we start from the material as it is: its symmetry broken by substitution, vacancies, or design.

Broken Symmetry Creates Function

  • Pristine TiS₂ is non-magnetic. A vacancy makes a local moment (PRB, 2026).
  • Pristine CeO₂ has no unpaired spins. An oxygen vacancy makes a Ce³⁺ spin centre (preprint).
  • Two different metals make the faces of a Janus MXene distinct, and one pair binds hydrogen near thermoneutrality (RSC Adv., 2026).

Broken Symmetry Limits Function

  • TiS₂ is a half-metallic ferromagnet only between 11% and 15% vacancies.
  • Mn in FeRh lowers the Curie temperature by about 450 K; Co keeps it (JMMM, 2026).
  • Re in FeCo raises magnetic damping almost fourfold (PRB, 2019).
When does one site tell you what a material will do, and when does the broken symmetry around it decide?

How We Compute

We compute random alloys directly with the coherent potential approximation, and individual defects with density functional theory. From the electronic structure we obtain the magnetic exchange interactions, and from those the temperature up to which magnetic order survives. When there are too many compositions to compute, interpretable machine learning narrows the search.

Tools and Collaborators

We build cview, an open-source crystallographic interface (paper), and test our predictions with experimental groups:

Research Pillars

Each pillar takes one consequence of broken symmetry in depth; machine learning serves all three.

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 Across the Pillars

A cross-cutting method serving the three disorder pillars: how do we use machine learning to discover materials without surrendering the physical insight that makes a discovery transferable?