Lan Li
Boise State University, ID·Materials Science
Quantum computers can solve some problems faster and with less energy than ordinary computers. However, most need extremely cold, quiet and dry conditions and large hardware.
- Fields
Materials Science
Computer Science
Chemistry
- Topics
molecular design
inverse design
machine learning
- Grants
- 1 known grant, through Aug 2027
What they work on
- Generate dye structure-property datasets using density functional theory (DFT) and time-dependent DFT, which simulate molecules and their light-excited states.
- Build MatFlow, a visual AI and machine-learning toolkit with a graphical interface for forward design (finding predictive features) and inverse design (proposing optimal dye structures).
- Validate and test the MatFlow toolkit with primary end users, then expand its user community beyond quantum computing.
More about the research
Dyes, conjugated organic molecules that absorb and emit light, could enable quantum computing at room temperature because of their optical properties. Finding the right dyes requires screening many candidates quickly, and that calls for better computational design tools.
Why it matters
Faster computational design of light-absorbing dyes could help identify molecules for room-temperature quantum computing. This could reduce the need for extreme cooling and bulky hardware. An easy-to-use AI design toolkit could also help researchers in chemistry, materials science, biophysics and data science explore new molecules more efficiently.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Farnaz Shakib
New Jersey Institute of Technology, NJ·Chemistry
Build a database of electrically conductive, magnetically bi-stable coordination polymers by inserting conjugated pillar ligands into conductive frameworks from the EC-MOF/Phase I database, using the group's CrySP Python package and density functional theory (DFT).
Carmen Lilley
University of Illinois Chicago, IL·Materials Science
Develop a machine-learning workflow that predicts elastic properties, stress-strain behavior and fracture strength of 2D lateral heterostructures with interface defects, at accuracy comparable to density functional theory (DFT).
Rohan Mishra
Washington University in St Louis, MO·Materials Science·Early-career
Identify low-loss chalcogenides with large optical anisotropy, meaning light behaves differently along different directions, using density functional theory and materials informatics to screen compounds containing transition metals.
The school
Boise State University: 10 labs on PI Pages, $58M a year on research, 64 research doctorates a year.
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