Maryland Energy Innovation Institute
Postdoctoral Research Associate, Massachusetts Institute of Technology, 2010-2013
Ph.D., University of Wisconsin-Madison, 2010
HONORS & AWARDS
Outstanding Young Scientist Award, Maryland Academy of Sciences, 2019
3M Non-Tenured Faculty Award, 3M, 2019
Scialog Fellow of Advanced Energy Storage, Research Corporation Foundation, 2017
Minta Martin Award, University of Maryland, 2014
- Computational materials science
- Computational materials design and materials discovery
- Molecular dynamics simulations
- Large-scale atomistic modeling
- Materials for energy storage and conversion
My research aims to advance the understanding, design, and discovery of engineering materials through cutting-edge computational techniques. We target critical materials problems that impede high-impact technologies, such as energy storage, conversion, and efficiency. In our research, the computational modeling provides enhanced fundamental scientific insights, and enables the ability to rationally design new materials.
Accelerated design and discovery of novel materials through computation. Computational techniques based on first principles are capable of predicting materials properties with little or no experimental input. In our research, we leverage an array of computational techniques to design new materials with enhancement in multiple properties. With the aid of supercomputers, computational methods can significantly speed up the innovation and development of new materials. Our current efforts focus on solid-state batteries, solid oxide fuel cell, and various membrane materials.
Selected publications: Joule, 2, 2016-2046 (2018); Nature communications, 8, 15893 (2017); Advanced Energy Materials, 1702998 (2018); Advanced Science, 1600517 (2017); Physical Chemistry Chemical Physics, 17, 18035-18044 (2015); Nature Materials, 14,1026–1031(2015); Energy and Environmental Science, 6, 148-156 (2013); Chemistry of Materials, 24, 15-17 (2012)
Understanding materials and interfaces in beyond Li-ion energy storage systems. The next-generation energy storage systems may be based on novel chemistries, such as all-solid-state, Li metal, Li-sulfur, and metal-oxygen, to achieve significantly higher energy density. Materials and their interfaces in these batteries are often the key limiting factors and origins of failures. For example, the degradation at the electrolyte-electrode interfaces causes poor cyclability, low coulombic efficiency, and premature failure in these new battery systems. We use state-of-the-art computation techniques to understand the limiting factors and failure mechanisms at the interfaces, and to computationally design solutions (such as novel coating materials) for these new energy technologies.
Selected publications: Joule, 2, 2016-2046 (2018); Journal of Materials Chemistry A, 4, 3253-3266 (2016) (Front cover); Advanced Science, 1600517 (2017); ACS Applied Materials & Interfaces, 7, 23685-23693 (2015)
Experimental collaborations: Nature Materials 16, 572-579 (2017); Advanced Energy Materials, 6, 1501590 (2016); Science Advances, 3, e1601659 (2017);Journal of the American Chemical Society, 138 (37), 12258–12262 (2016); Advanced Materials (2017); Nature Communications, 7, 11441 (2016); ACS Nano, 10, 9577–9585, (2016); Nano Letter, 15, 5755–5763 (2015)
Large-scale atomistic modeling and molecular dynamics. Large-scale atomistic modeling has the unique capability to capture complex materials phenomena, ranging from interfaces, nanostructures, to non-equilibrium dynamics. However, current large-scale modeling methods based on classical force fields have limited accuracy, transferability, and predictivity, while higher level ab initio methods are often limited in system size (hundreds of atoms) and time-scale (tens of ps). We aim to bridge the gap between ab initio methods and large-scale atomistic modeling. Integrating these techniques across different length scales enable us the unique capability to study complex processes with full atomistic details.
Selected publications: Nature, 457, 1116-1119 (2009); Nature Materials, 12, 9-11 (2013); Journal of Physics D: Applied Physics, 44, 405401 (2011); Applied Physics Letters, 90, 181926 (2007)
- ENMA 461: Thermodynamics of Materials
- ENMA 400 / ENMA 600: Introduction to Computational Materials Science (Formerly as ENMA 489A/ENMA 698A)
- ENMA 300 / ENME 382: Introduction to Materials and Their Applications