(2fo) Engineering Exotic Correlated Disorder for Functional Amorphous Soft-Matter Systems

My vision for a research program is one specializing in the rational and inverse design of functional 
amorphous soft materials, in particular polymeric and low-dimensional materials, with a unique focus 
on materials with exotic correlated disorder that will differentiate my group from other research groups 
working on computational design of functional soft materials. The rational and inverse design strategy 
in the context of soft materials generally involves using an optimization scheme to efficiently 
search for optimal combinations of design parameters that realize certain targeted (multi)functionalities 
given the materials constraints, and is in principle a strategy much more efficient and less costly than 
the traditional trial-and-error strategy, but currently often suffers from a formidably high-dimensional 
design space. Moreover, for many amorphous soft-matter systems, quantitative tools that can systemically 
distinguish different types of order are either lacking or not widely known. My group will fill the gap 
and develop an integrated platform that utilizes state-of-the-art theoretical, computational, and machine 
learning toolkits to discover, design, and generate novel functional amorphous soft materials with desirable 
physical properties that cannot be achieved by crystalline or completely random systems.

My past experiences have put me in a unique position to achieve this vision. In particular, my most 
important achievements during Ph.D. with Prof. Salvatore Torquato at Princeton include developing a wide 
variety of novel techniques to computationally design and generate/construct experimentally realizable 
disordered hyperuniform materials, which are exotic states of matter that possess a special type of 
correlated disorder with a hidden long-range order. Moreover, these materials are endowed with novel and 
desirable photonic, phononic, transport and mechanical properties, and wave-propagation characteristics, 
enabling unique applications (e.g., waveguides without directional constraints) beyond the capabilities 
of crystalline or completely random materials. In addition, I developed the first predicative 
computational model of tumor dormancy, and our work was featured in Top News on Princeton University 
homepage. In a series of independent research between Ph.D. and postdoc, in collaboration with multiple 
research groups, I pioneered the extension of the disordered hyperuniformity concept to atomic-scale 
materials, which is poised to become a promising independent research (sub)field. During my postdoc with 
Prof. Glenn H. Fredrickson at University of California, Santa Barbara, I have devised a new machine 
learning framework that incorporates a set of efficient low-dimensional structural 
representations to accurately predict energetics and structures of polymer mesophases.

Teaching Interests

Teaching and mentoring was an essential component of my academic training, which proved to be a very 
rewarding experience for myself. My goal in teaching is to not only convey knowledge, but also teach 
students how to be good learners, questioners, and problem solvers, and thus I prefer teaching in a 
highly interactive manner with students. 

Although my Ph.D. degree is officially in chemistry, the particular research group I graduated from 
is a highly interdisciplinary one that is based in the Department of Chemistry, the Princeton Institute 
for the Science and Technology of Materials, and the Princeton Center for Theoretical Science, and also 
has affiliations with three other departments/programs: Physics, Applied and Computational Mathematics, 
and Mechanical and Aerospace Engineering. I was mainly trained as a soft-matter theorist, and gained a 
deep understanding of structure-property relationships, transport phenomena as well as optimization 
techniques. Moreover, the physics chemistry courses I took and taught during my undergraduate and 
graduate school years had a heavy component in chemical kinetics and reaction dynamics, which are 
also key topics in chemical engineering and materials science. In addition, during my years at 
Carnegie Mellon University, I took a variety of graduate-level machine learning courses, and acquired
 good knowledge of various state-of-the-art machine learning techniques. As a result, I am comfortable 
with teaching the following chemical engineering core courses:

1) chemical engineering/materials science thermodynamics;

2) chemical engineering kinetics and reaction engineering, or kinetics of materials;

3) statistics and mathematics courses for chemical engineers and materials scientists.

Moreover, I will be able to develop a graduate-level machine-learning course tailored for chemical 
engineers and materials scientists, and I am also interested in teaching a graduate-level course 
covering the various particle-based and field-based simulation techniques commonly used in chemical 
engineering and materials science research.