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)


6/1/22: Our paper on Machine Learning for Rare-earth Separation has been accepted in JACS Au. Congrats, Tongyu!

PI: De-en Jiang

Professor of Chemistry

Tel: (951) 827-4430

djiang at ucr.edu

Current Research Topics:

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

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

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

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


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.

Network     structures      in UCl3-NaCl and UCl4-NaCl from first principles MD