(4an) Multiscale Modeling for Hierarchical Materials Design | AIChE

(4an) Multiscale Modeling for Hierarchical Materials Design


Qi, X. - Presenter, University of Washington
Research Interests

The emergence of modern bottom-up hierarchical nanomaterial design has tremendously advanced the efficiency of functional materials in energy-, environment- and biomedicine-related applications. As properties of the higher-level structure are governed by the geometry and spatial arrangement of the lower-level building blocks, major challenges not only arise from a continuing lack of thorough understanding of properties at each length scale, but also lie in connecting macroscopic observables to microscopic events.

In my graduate research at the Pennsylvania State University, I worked with Prof. Kristen Fichthorn on understanding the role of structure-directing agents (SDAs) in shape-controlled nanocrystal growth, from both thermodynamic and kinetic perspectives. Our work highlighted the combination of atomistic molecular dynamic (MD) simulation and theories to probe the complex linkage between the facet-selectivity of the SDAs and the resulting shape outcomes. Besides establishing an overarching framework, we also developed methods for properties that lack available means to obtain. Specifically, for the thermodynamic perspective, we (1) developed a novel method to calculate the interfacial free energy for multi-component molecular solid-liquid interfaces, which determines the thermodynamic equilibrium shape under the Wulff construction. For the out-of-equilibrium regime, we demonstrated that (2) kinetic control can be achieved by the SDAs through regulating solution-phase atom deposition, and (3) even without SDA, intrinsic properties of the nanocrystal, such as strain due to twinning, can result in “super-highways” to direct surface adatom diffusion that can ultimately achieve structures with high aspect ratios, like facile nanowires.

Extending beyond the influences imparted from the selective binding and the scope of a single nanocrystal, my post-doc research with Prof. Jim Pfaendtner at the University of Washington explores both downward to the origin (i.e., to interpret the different binding affinity at the molecular level) and upward to the collective behavior (i.e., molecule-functioned self-assembly of nanoparticles). Using atomistic MD simulation with metadynamics sampling, we have unraveled the origin of (1) facet-selectivity of amphiphilic peptoids on Au and (2) pH-dependent binding of an engineered peptide Car9 to silica surfaces. We further (3) utilize the binding mechanism of peptoid/peptide on solid surfaces and develop an algorithm that significantly speeds up binding free-energy predictions, to accommodate the ongoing demand of simulation-leading molecular design. On the other end of the spectrum, we (4) combine colloidal theory and MD simulation and construct a highly coarse-grained rigid-body model with rigorously obtained interactions to understand and predict reversible self-assembly of silica nanoparticles mediated by a bifunctional silica-binding protein.

As an independent investigator and faculty member, I plan to integrate my aforementioned expertise into a coherent research program that will realize my vision for advancing multiscale modeling of hierarchical materials in energy- and health-related applications. The initial research directions in my group will address the following areas: (1) equilibrium and out-of-equilibrium assembly of structured nanocrystals for energy harvesting and transfer, which spans from molecular interaction to mesoscale structural control, as well as machined-learned functional molecule and external field design; (2) resolving the complex interfacial structures at liquid-semiconductor interfaces using quantum mechanics calculation, reactive MD simulation and classic MD simulation, with the ultimate goal to finely tune their interfacial properties, shape-controlled synthesis and the morphology of mesocrystal, and (3) assembly and disassembly of protein nanocage via high-precision dynamic intervention for biomedical applications, through multi-scale simulation schemes that allow for both high-speed large-scale simulations and physical models with high fidelity to experiments.

Teaching Interests

While the definition of an outstanding educator is multi-faceted, its core, in my opinion, should be an effective presenter and supportive mentor. From the standpoint of a college student, I learned the most from instructors who were able to deliver new information with seamless logic and high clarity. Later as I gained more experience after assisting core chemical engineering classes and mentoring several undergraduate and graduate researchers, I have come to recognize that effective teaching is a total reflection of a coherent understanding of the science, as well as the ability of perspective-taking. The first component ensures the quality of the materials and the latter bolsters the effectiveness in the delivery of knowledge. Therefore, a strong teacher should also share several key characters of a supportive mentor, who would designate time and effort to profile the students, understand their needs, and thus know the best way to motivate engagement in active learning.

During the Winter quarter of 2021, I was fortunate to independently teach a graduate-level course as the main instructor at the University of Washington and put my teaching philosophy into practice. This course was a part of special topic series which focused on current trends in chemical engineering. During this 10-week period, my colleague and I lectured the course collaboratively to introduce molecular dynamics and quantum mechanics to new graduate and senior undergraduate students. Extending beyond classroom teaching, my responsibility as the lead instructor also included collecting topics, organizing the course structure, designing assessments criteria, mentoring students, setting up a communication platform for vibrant discussion, and coordinating with the administrative branch.

Moving forward, I want to continue to serve in training the next generations of chemical engineers. My undergraduate and graduate education in chemical engineering allow me to confidently teach a broad set of undergraduate-level courses, including mass and energy balances, thermodynamics, kinetics, heat and mass transfer, process control, biochemical engineering, etc. If given the opportunity, I would like to design a course that highlights chemical process safety in manufacturing and regulations and work ethics in the new digital era. For graduate level courses, I would like to integrate my expertise in teaching core courses like statistical mechanics, kinetics, and elective courses that focus on colloids or computational methods in chemical materials.