(2ao) Nanoporous Materials for Energy, Healthcare, and Sustainability

Porosity is ubiquitous, from naturally formed systems (e.g., shale rocks, wood, and human bones) to synthetic materials (e.g., molecular cages and metal-organic frameworks). Nanoporous materials (NPMs) are unique in providing a tailored pore environment and interfaces down to the nanoscale, and they stand out as promising candidates in a wide spectrum of applications, such as energy storage, chemical separations, carbon capture, sensing, catalysis, nano-manufacturing, drug delivery, and tissue regeneration. While significant progress has been made in the past decades in the discovery, synthesis, and characterization of advanced NPMs, the holistic picture of the materials’ “genome” that controls their properties remains far from well-understood. Knowledge about the nature of NPMs, the thermophysical behavior of confined phases, and the interplay between them is key to realizing the full potential of advanced porous materials towards a cleaner, healthier, and more sustainable future for humankind.

Molecular simulations and statistical mechanical theories are central in the efforts to achieve an atomic-level understanding of confined systems. On the other hand, machine learning (ML), provides a data-based solution to revealing important relationships between quantities (e.g., a material’s structure and its adsorption performance) that are difficult to be discovered with conventional methods.

The overarching goal of my independent research program is to better understand the nature of nanoporous adsorbents and their unique confinement and surface effects on nanophases and reactions by integrating physics-based and data-based methods. I will focus on three directions: (1) I will develop novel theoretical methods for the experimental characterization of NPMs. I hope to integrate our development into the self-driving laboratory to reduce the existing bottleneck in the high-fidelity materials characterization. (2) I will develop novel ML algorithms for accelerated materials discovery and design. I will target applications in carbon capture, chemical separations for clean energy storage, and the synergy of molecular diffusion and catalysis. (3) I will investigate fundamental questions associated with the confined phases and reactions with a main focus on the microscopic pressure tensor. I am interested in applying the new knowledge to better predict the nucleation and crystallization of confined drug molecules for long-term drug delivery applications.

My previous research training has prepared me with a solid foundation for my future proposed research. In my graduate research with Profs. Keith Gubbins and Erik Santiso, I developed an advanced statistical mechanical theory [1] and model [2] for gas adsorption on geometrically and energetically heterogeneous surfaces. My development significantly simplifies the theoretical treatment of real heterogeneous surfaces without sacrificing model accuracy for predicting adsorption isotherm. It also paves the way for computational modeling of adsorption in larger temporal and spatial scales. Motivated by high-pressure conducting materials formed in carbon nanotubes at ambient pressure, I pioneered the fundamental understanding of the local pressure tensor in thin adsorbed films. I showed, for the first time, that it is possible to uniquely define a microscopic pressure tensor, a property that had been considered ambiguous since 1950 [3]. I also solved a serious barrier to the computation of the pressure tensor for molecules having long-range Coulombic interactions [4]. Our development lays the foundation for the next-generation high-pressure manufacturing of pharmaceuticals and materials.

In my postdoctoral research with Prof. Randall Snurr, I developed a physically inspired way to better represent nanoporous materials for ML using energy-based descriptors [5]. The developed ML model allows us to identify promising materials among a myriad of candidates with thousands of times speedup over typical molecular simulations. In addition, I co-led the development of a first-of-its-kind, free online adsorption database, MOFDB (https://mof.tech.northwestern.edu/). This database has been accessed by over 3500 users from 67 countries since 2020.

Selected Publications (Published: 16 total, 7 first-author, 3 corresponding author)

[1] K. Shi, E.E. Santiso, and K.E. Gubbins, “Conformal Sites Theory for Adsorbed Films on Energetically Heterogeneous Surfaces,” Langmuir, vol. 36, no. 7, pp. 1822–1838, Feb. 2020.

[2] K. Shi, E.E. Santiso, and K.E. Gubbins, “Bottom-Up Approach to the Coarse-Grained Surface Model: Effective Solid–Fluid Potentials for Adsorption on Heterogeneous Surfaces,” Langmuir, vol. 35, no. 17, pp. 5975–5986, Apr. 2019.

[3] K. Shi, E.E. Santiso, and K.E. Gubbins, “Can we define a unique microscopic pressure in inhomogeneous fluids?,” J. Chem. Phys., vol. 154, no. 8, p. 084502, Feb. 2021.

[4] K. Shi, Y. Shen, E.E. Santiso, and K.E. Gubbins, “Microscopic Pressure Tensor in Cylindrical Geometry: Pressure of Water in a Carbon Nanotube,” J. Chem. Theory Comput., vol. 16, no. 9, pp. 5548–5561, Sep. 2020.

[5] K. Shi, Z. Li, D. Anstine, D. Tang, C. Colina, D. Sholl, J.I. Siepmann, R.Q. Snurr. “Two-dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials”, Journal of Materials Chemistry A, under review, 2022.

Teaching Interests:

My teaching philosophy can be summarized as “Teaching as a student”. I believe that teaching can be more effective if the instructor considers more from learners’ perspectives. This learner-centered teaching requires understanding the learners’ needs and backgrounds, encouraging the students to be active learners, and improving teaching by collecting students’ feedback. I arrived at this teaching strategy through more than 5-year experience as a teaching assistant and guest lecturer, and through active interaction with both students and instructors in the class. During my Ph.D., I have presented 18 guest lectures in Graduate Thermodynamics to more than 200 graduate students, most of whom are first-year Ph.D. students. I have also had a chance to present 3 guest lectures in Undergraduate Thermodynamics. Based on my teaching evaluations, I received Linde Exceptional Teaching Assistant Award (2016) and Mentored Teaching Fellowships (2016-2018) at North Carolina State University. After graduation, I was invited to give a guest lecture on molecular modeling at Carnegie Mellon University. These experiences had well prepared me to build a diverse, energetic, and effective learning environment in the class.

My teaching expertise includes undergraduate and graduate level thermodynamics, transport, reaction engineering, and process modeling (advanced mathematics). In addition to core courses, I would like to bring cutting-edge machine learning and molecular modeling to both undergraduate and graduate elective courses. I believe future leaders in chemical engineering should be able to leverage diverse tools and expertise to solve challenging problems.

Selected Awards

Department of Energy Team Science Contest Winner (Team Leader), 2021.

James K. Ferrell Outstanding Ph.D. Graduate Award, NC State University, 2020.

AIChE’s CoMSEF Graduate Student Award, 2019

FOMMS Poster Prize, Foundations of Molecular Modeling and Simulation (FOMMS) Conference, 2018

Outstanding Poster Presentation Prize, 8th International Workshop on Characterization of Porous Materials, 2018

Mentored Teaching Fellowships, NC State University, 2016 – 2018

Linde Exceptional Teaching Assistant Award, NC State University, 2016