Mingjie Liu, Ph.D. to present "Computational design of catalytic materials at atomic scale for electro-chemical energy conversion"

Mingjie Liu, Ph.D. to present "Computational design of catalytic materials at atomic scale for electro-chemical energy conversion"

Abstract: Electrocatalysis plays a crucial role in advanced energy conversion. Developing innovative catalysts with low cost, high activity and selectivity are critical and desirable. The few-atoms catalysts confined in two-dimensional hosts have shown great potentials as electrocatalysts for CO2 reduction reactions, hydrogen evolution reaction etc. The first-principles simulation such as density functional theory is a useful tool to understand the reaction mechanisms, the activity and selectivity of the catalysts. It also provides physical insights and new principles such as descriptors which enable us to design new catalysts for versatile reactions. In this talk, I will show two examples including graphene based materials for CO2 reduction reactions and MoS2 based materials for hydrogen evolution reaction. Through those examples, I will demonstrate how the computational efforts can be successfully integrated with materials synthesis, characterization and measurement to develop new catalysts. In the end, I will briefly discuss my research interests on how to combine atomistic simulations with machine learning methods to further accelerate the new catalysts design and discovery process.

About: Dr. Mingjie Liu obtained her Ph.D. in Materials Science and NanoEngineering at Rice University in 2016. She is currently a postdoc research associate in the Center for Functional Nanomaterials at Brookhaven National Laboratory. Her research background includes theoretical predictions on the structures and properties of one-dimensional atomic chains; computational study on two dimensional materials as energy materials such as batteries and electrocatalysts. Her current research interests are computational development of few-atoms electrocatalysts for CO2 reduction reaction and nitrogen reduction reaction. She is interested in combining the machine learning methods and atomistic simulations to accelerate innovative catalysts design.