Ramon Miranda Quintana
University of Florida, FL·Chemistry·Early-career
Computers can predict how molecules and materials behave, which saves many costly experiments, and they can sort through huge collections of data. Both tasks hit hard limits.
- Fields
Chemistry
Computer Science
Biophysics
- Topics
quantum chemistry
electronic structure theory
strongly correlated systems
- Grants
- 2 known grants, through Mar 2030
What they work on
- Design and test new wavefunction ansätze, trial mathematical forms for electron behavior, for multi-reference problems, using the FANCI framework to combine configuration interaction and coupled cluster with quasi-particle, symmetry-breaking and seniority ideas.
- Develop a flexible perturbation theory framework that can refine any multi-determinant wavefunction and compute reduced density matrices and response functions for molecular properties.
- Extend these methods to realistic strongly correlated compounds with wavefunction-in-wavefunction embedding, which treats a key region in detail, to model spin-state changes and redox behavior.
- Use extended similarity indices, which compare many molecules at once, to map relationships among very large molecular libraries and measure chemical diversity to guide generative drug design and repurposing.
- Build medoid, clustering and sampling tools that find near-folded and metastable protein states in very long molecular dynamics simulations to explore protein folding landscapes.
- Identify chemically related tissue regions in imaging mass spectrometry data with similarity indices and apply them to lipid expression in pancreatic tissue from type 1 diabetes.
More about the research
Standard quantum chemistry methods fail badly for 'strongly correlated' systems, where many electrons interact intensely, such as compounds with several metal centers. And standard ways of comparing data can only compare two objects at a time, which becomes far too slow for modern datasets. The lab works on both problems by building new mathematical and computational methods. One project develops quantum chemistry methods for difficult electronic structures. The other develops fast similarity tools for large biomedical datasets.
Why it matters
Reliable methods for strongly correlated systems could help chemists design organometallic catalysts, energy-conversion devices and high-density data-storage materials without as much trial and error. Fast many-object similarity tools could speed virtual screening for drug leads and drug repurposing. They could also help interpret protein folding simulations and turn tissue mass spectrometry images into diagnostic insight, including into type 1 diabetes.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Evangelos Miliordos
Auburn University, AL·Chemistry
Model solvated electron precursors joined by molecular bridges (linked-SEPEs) using high-level multi-reference and density functional calculations to reveal how their diffuse electrons behave.
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.
Cyrus Dreyer
Stony Brook University, NY·Physics·Early-career
Develop quantum embedding methods that combine density-functional theory with many-body methods, improving how they treat electron repulsion in defect orbitals, defect–crystal mixing, and double-counting errors.
The school
University of Florida: 65 labs on PI Pages, $1.3B a year on research, 795 research doctorates a year.
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