Our group 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, 14,1026–1031Energy 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

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)

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