Research Overview
My research is organized around three complementary threads: first-principles rigor, applied to quantum materials, accelerated by machine learning. I use density functional theory, many-body perturbation theory, and related first-principles methods to study the electronic and lattice properties of materials where quantum effects are both scientifically rich and technologically consequential.
At Berkeley Lab I work at the intersection of fundamental condensed matter physics and quantum device engineering. My immediate focus is superconducting materials for qubit fabrication — understanding the microscopic origin of their properties and identifying pathways to improved device performance. In parallel, I investigate topology in amorphous systems, where the interplay between disorder and non-trivial band geometry poses deep theoretical questions. A growing part of my work applies machine learning to extend first-principles accuracy to length and time scales that brute-force calculation cannot reach.
I collaborate closely with experimentalists at the Molecular Foundry and the Materials Science Division, and I am interested in translating this computational toolkit into industrial R&D settings in quantum computing, semiconductor materials, and AI-driven materials design.
Theme 1
Superconducting Materials for Qubits
First-principles study of superconducting properties in metal nitrides and related materials relevant to qubit fabrication. In collaboration with experimentalists at Berkeley Lab, I am revisiting the instability and superconductivity of δ-NbN and exploring theoretical designs for phonon filters used in quantum sensing and qubit architectures.
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Topology in Amorphous Quantum Materials
Diagnosing non-trivial topology in disordered and amorphous systems from first principles, with a focus on amorphous Bi₂Se₃ and quantum phase transitions of topological edge states. This work probes how topological protection survives — or fails — in the absence of long-range crystalline order.
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Machine Learning for Materials Discovery
Developing machine-learning interatomic potentials for amorphous materials, and applying ML to accelerate large-scale simulations and materials-property prediction. The goal is to extend first-principles accuracy to system sizes and timescales that brute-force DFT cannot reach.
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