Johnpierre Paglione
University of Maryland, MD·Physics
Materials scientists usually look for perfect crystals with as little disorder as possible. High-entropy materials take the opposite approach.
- Fields
Physics
Materials Science
Chemistry
- Topics
high entropy materials
quantum materials
configurational entropy
- Grants
- 1 known grant, through Sep 2029
What they work on
- Discover new High Entropy Quantum Materials using computational prediction and machine-learning-assisted searches within the AFLOW materials framework, combined with high-throughput synthesis.
- Apply configurational entropy to spinel oxides, transition metal dichalcogenides, half-Heuslers, and ThCr2Si2-type compounds to tune magnetic, topological, and correlated electron phases.
- Determine how entropy stabilizes new phases and affects low-temperature properties, and establish ways to distinguish true high-entropy materials from ordinary variants.
- Build a dedicated HEQM data repository that curates synthesis and property data for public access and data mining.
More about the research
They mix many elements at random within one crystal, and this disorder, called configurational entropy, can stabilize the structure and improve its properties. This project aims to start a new subfield, High Entropy Quantum Materials (HEQMs). It asks whether deliberate disorder can create or tune quantum behaviors such as magnetism, topological states, and correlated electron phases, in which electrons interact strongly with one another.
Why it matters
Showing that disorder can be used to tune magnetic, topological, and strongly interacting electronic states could greatly widen the range of materials available for quantum technologies. The project's open data repository could speed materials discovery for physicists, chemists, and engineers building future quantum and electronic devices.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Qi An
Iowa State University, IA·Materials Science
Build a closed-loop AI platform that compares Bayesian optimization (choosing the most informative next test) with deep reinforcement learning (trial-and-reward learning) for designing the high-entropy carbide (Ti-Zr-Hf-Nb-Ta)C, using machine-learning force-field atomistic simulations.
You Zhou
University of Maryland, MD·Physics·Early-career
Fabricate coupled bilayers of transition metal dichalcogenide semiconductors and test how their atomic structure and stacking affect the stability of Wigner crystals, ordered patterns of electrons.
Nirmal Ghimire
University of Notre Dame, IN·Physics·Early-career
Synthesize kagome lattice magnets, materials with magnetic atoms on a corner-sharing triangle network, and use magnetotransport measurements to study how topological effects in real and momentum space interact.
The school
University of Maryland: 49 labs on PI Pages, $1.5B a year on research, 602 research doctorates a year.
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