Our research focuses on computational materials chemistry and nanoscience, with a long-term goal to achieve data- driven design of functional materials and molecules for a sustainable society.
(Note: Our group is moving to Vanderbilt University ChBE Dept on July 1, 2022)
Headline:
6/1/22: Our paper on Machine Learning for Rare-earth Separation has been accepted in JACS Au. Congrats, Tongyu!
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Current Research Topics:
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Computational nanocatalysis: Nanoclusters, single atoms, oxides, perovskites, zeolites, 2D materials
Simulations of molecular and ionic separations via membranes, sorbents, composite systems, and ionic liquids for carbon capture and rare-earth separations
First principles understanding of electrical energy storage and solid/liquid interfaces
Understanding physical and chemical properties of molten salts from molecular dynamics for nuclear energy applications
Moore’s Law Meets Materials Chemistry via Quantum Mechanics, Classical Mechanics, and Machine Learning. We aim to address the following materials chemistry challenges with computational tools.
Important challenges in nanocatalysis
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Convert abundant small molecules to fuels and value-added chemicals
We use electronic structure methods such as DFT coupled with transition-state search to understand and predict catalytic pathways
Catalysts of special interest include gold nanoclusters, 2D materials, transition-metal oxides, and bimetallic materials
CO2 reduction on a Cu cluster
Deep learning of hydride locations
Materials for gas separation
Important for chemical industry
Sorbents and membranes are most commonly used
We study local interaction of gas and separation media with quantum chemistry
We model solubility and diffusivity with molecular simulations including Monte Carlo and molecular dynamics
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Advanced membranes
Ligand design and molecular simulations for rare-earth separations
Important for critical materials needs
Coordination chemistry, solvation, and interfacial phenomena
Data-driven predictive modeling of distribution ratios and separation factors via machine learning
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Electric energy storage
Broad applications in transportation, electronics, and robotics
We work on supercapacitors, including double-layer and pseudo capacitors
We use joint DFT to study the charging behaviors of different materials including advanced carbons and MXenes
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MXene
Charge storage in H2SO4
Molten salt chemistry for nuclear energy
Molten-salt reactors (MSEs) offer many advantages over the conventional light-water reactors.
Many thermophysical, thermochemical, and transport properties of molten chloride salts relevant to fast-spectrum MSEs are not available.
We use MD simulations to predict structure/coordination, spectral features, and thermophysical properties of molten chlorides.
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Network structures in UCl3-NaCl and UCl4-NaCl from first principles MD
