Education

• B.S., Chemical Engineering, National Taiwan University, 2002-2006
• M.S., Chemical Engineering, National Taiwan University, 2006-2007
• Ph.D., Chemical Engineering, University of California-Berkeley, 2010-2014

Key Honors and Distinctions

• DOW Excellence in Teaching Award, 2013
• Chevron Fellowship, 2013
• Graduate Student Research Award, AICHE Separation Division, 2012
• Presidential Awards (six awards), National Taiwan University, 2003-2005

Recent Publication Honors

Research featured on the front or back covers of The Journal of Physical Chemistry C, ChemPhysChem (back cover), and Molecular Systems Design & Engineering.

RESEARCH AREAS – Lin Group for Computational Materials Discovery

• Large-scale computational screenings: Use molecular simulations to discover novel, more energy-efficient and cost-effective materials for energy-related applications. 

• Methodology development: Integrate multi-scale computational techniques to achieve more accurate simulation predictions and more efficient screenings. 
• Graduate student research opportunities are currently available.

*PUBLICATIONS – Please visit Google Scholar and our Publications page.

Research description

Our research interests lie in the energy field with the aim of discovering new materials to achieve more energy-efficient and cost-effective energy-related processes including separations, energy storage, etc. Reducing energy consumption, cost, and CO2 emissions of many energy-related processes is currently one of the most prominent challenges. Recently, porous materials including zeolites, zeolitic imidazolate frameworks (ZIFs), metal-organic frameworks (MOFs), graphene-based materials, etc. have become of great interest to the scientific community for their potential in energy applications. Can we operate these processes with better energy efficiencies and at lower costs?

To achieve this, discovering new materials is essential. The total number of possible material candidates, however, could be hypothetically infinite. For instance, MOFs are highly tunable; one can design an optimal material by having the right combination of chemical compositions and structural topologies. Computational approaches allow us to efficiently and accurately study a large number of materials to identify promising materials as well as provide a better atomic-level understanding of material properties. The identification and understanding of promising materials can substantially help guide and accelerate the development of new materials. In addition, to facilitate the materials discovery, we aim to collaborate with researchers from different fields including materials synthesis/characterization, process engineering, quantum chemistry, etc., to synergistically push materials development forward.