Computational materials science
From microscopic mechanisms to materials design
Y-Matter Lab studies how atomic structure, electronic states, magnetism, lattice dynamics, defects, and interfaces give rise to useful material properties. We combine first-principles calculations, atomistic simulations, and machine learning to understand existing materials and identify promising new candidates.
Materials
Major Research Lines
These lines are connected by a common goal: to reveal design principles that can guide new experiments and new materials.
Quantum Materials
We study superconductors, hydrides, two-dimensional systems, polar metals, magnetic materials, and oxide interfaces where electronic, structural, magnetic, and vibrational degrees of freedom are strongly coupled.
- Electron-phonon coupling, anharmonicity, and superconductivity.
- Pressure, strain, dimensionality, and chemical tuning of quantum phases.
- Microscopic mechanisms behind emergent electronic and magnetic responses.
Information Materials
We investigate ferroelectric, ferromagnetic, piezoelectric, and multiferroic materials for electronics and spintronics. Much of this work focuses on how polarization, magnetism, defects, and interfaces can be controlled.
- Ferroelectric switching, tunnel junctions, and photovoltaic response.
- Multiferroics, magnetoelectric coupling, and magnetic anisotropy.
- Complex oxide thin films, heterostructures, vacancies, and interfacial reconstruction.
Energy Materials
We use theory and simulation to explore materials for thermoelectric conversion, catalysis, energy storage, and energy harvesting. A central theme is the relation between local structure, lattice dynamics, transport, and functional response.
- Thermoelectrics with low thermal conductivity and favorable electronic structure.
- Negative thermal expansion and thermal transport controlled by phonons.
- Polar, defective, surface, and interface effects in energy-related processes.
Methods
How We Work
First-Principles and Atomistic Simulations
Density functional theory, phonon calculations, lattice dynamics, molecular dynamics, and finite-temperature simulations connect composition and structure with stability, transport, phase transitions, and electron-phonon physics.
Materials Informatics
Machine learning and data-driven workflows help us search large materials spaces, build structure-property models, and prioritize candidates for detailed study, especially when available data are limited.
Theory-Experiment Integration
Computation is used to interpret measurements, test mechanisms, and suggest new compositions, strain states, defects, interfaces, and growth conditions for experimental collaborators.
For prospective students
What You Can Learn Here
Students can build skills in quantum-mechanical materials modeling, scientific programming, data analysis, machine learning, and physical interpretation. Projects are often collaborative, with room to move from careful mechanism-building to exploratory materials discovery.