Aldo Romero
West Virginia University, WV·Materials Science
Strongly correlated materials are solids whose electrons interact so strongly that simple theories fail to predict their behavior. They are central to future technologies in quantum computing, spintronics (electronics that use electron spin) and magnetic storage.
- Fields
Materials Science
Physics
Computer Science
- Topics
strongly correlated materials
magnetism
magnetic simulations
- Grants
- 1 known grant, through Aug 2028
What they work on
- Extend the DMFTwDFT code into a unified open-source framework combining DFT, DFT+U and dynamical mean field theory on the VASP and SIESTA codes, supporting both collinear and noncollinear magnetic configurations.
- Implement spin-dependent Green's functions, Bethe-Salpeter magnetic susceptibility calculations and perturbative methods to extract spin-exchange interactions, and link them to spin tools such as TB2J and Vampire.
- Automate high-throughput magnetic simulations within AiiDA, compare DFT+U and DMFT magnetic parameters, and validate predictions against neutron scattering and ARPES data.
More about the research
Their magnetism is hard to capture with standard static theories or with experiments alone. Better simulation tools would help researchers predict and design these materials.
Why it matters
Accurate predictions of magnetism in strongly correlated materials could help researchers find and design materials for quantum computing, spintronic devices and magnetic data storage. Providing these capabilities as free, automated, open-source software lets more research groups run high-precision magnetic simulations and screen many candidate materials.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Yubo Yang
Hofstra University, NY·Physics
Discover and characterize strongly correlated electronic phases in two-dimensional moiré materials, such as generalized Wigner crystals, using many-body wavefunction methods that go beyond mean-field theory.
Jedediah Pixley
Rutgers University-New Brunswick, NJ·Physics
Determine how twist-angle disorder, the place-to-place variation in twist, alters electronic response in twisted bilayer graphene and twisted transition-metal dichalcogenide bilayers, using kernel polynomial calculations on large disordered models.
Yubo Qi
University of Alabama at Birmingham, AL·Physics
Develop a deep-learning-based, multifunctional computational method that reproduces density functional theory predictions of electron behavior while scaling to systems far larger than direct calculations allow.
The school
West Virginia University: 15 labs on PI Pages, $259M a year on research, 201 research doctorates a year.
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