WELCOME TO THE COMPUTATIONAL

LED BY PROF. DE-EN JIANG @ Vanderbilt ChBE since July 1, 2022

CHEMICAL SCIENCES AND MATERIALS  LABORATORY

Our research focuses on computational chemical science and materials,   with a long-term goal to achieve data-driven design of functional materials and molecules for a sustainable society.

Headline:


5/20/2024: Yuqing sucessfully defended her dissertation on "Computational Insights into Materials for Sustainability". Congrats, Dr. Fu. (doi)

PI: De-en Jiang

Professor of Chemical and Biomolecular Engineering

Tel: (615) 343-3531

de-en.jiang at vanderbilt.edu

Current Research Topics:

Computational nanocatalysis: Nanoclusters, single atoms, oxides, perovskites, oxyhydrides, zeolites, supported metals, high-entropy oxides

Computational seperation science: Simulations of molecular and ionic separations via membranes, sorbents,  composite systems, and ionic liquids for carbon capture and rare-earth separations; machine learning approach

Computional materials chemistry for batteries: First principles understanding and exploration of anion-storage batteries and cathode materials and electrolytes

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 single atoms, nanoclusters,  complex oxides, and high-entropy systems

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

Monolayer fullerene membranes (doi)

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 anion-storage batteries, composite cathode materials, and advanced electroyltes

We use DFT and machine-learning potentials to study the charging behaviors of different cathode materials including  oxides and lithium-salt composites as well as ion transport

Computional interfaces: Understanding the organic-inorganic interfaces and functionalization for MXenes and other 2D materials

Li-ion transport in LiTaCl6 via machine-learning force-field molecular dynamics simulations (doi)

Amine-derived ligands on MXene surfaces (doi)