(4ez) Using Atomistic Simulations and Machine Learning Technology to Discover New Porous Materials for Sustainable Energy Applications

Overview

With recent improvements in computational capacity which has made high performance computing an affordable option, molecular dynamics (MD) modeling described by classical Newtonian equations coupled with semi-empirical force fields can be used to investigate systems ranging from biomolecules, such as proteins and polymers, to small molecules, and provide statistically reliable results with spatial resolution at the sub-Angstrom scale, and temporal resolution at the femtosecond level. Chemists and chemical engineers have always been trying to elucidate nano-scale behaviors of materials. Computational molecular modeling tools are the ideal candidate to provide insights at a molecular level that can aid in the understanding and developing new materials. Besides the ability to provide fundamental understanding, modeling methods also find wide applications in: predicting behaviors under extreme conditions, such as high temperature and pressure; studying toxic chemical and biological agents which are dangerous in the laboratory; investigating properties or processes that cannot yet be studied experimentally (e.g., many nanoscale system properties); testing the limitations of various empirical laws and finally to understand unexpected observations in experimental studies.

PREVIOUS AND CURRENT RESEARCH

I am currently a postdoctoral fellow in the group of Prof. Edward Maginn at University of Notre Dame. Primarily, in our research area, various molecular modeling methods are applied to study materials, including ionic liquids, deep eutectic solvents, and zeolites, which are widely used chemical engineering processes, especially in catalysis and energy storages. I am deeply involved in three projects: (1) collaborating with experimental experts and using molecular dynamics simulations to investigate thermophysical properties of phenol-derivative-based deep eutectic solvents; (2) developing a classical molecular model to understand how organic structure-directing agents (OSDA) affect the distribution of Al atoms in zeolite catalysts; (3) understanding structural, dynamic, and electrical properties of new types of ionic liquids which consists of lithium and some asymmetric anions.

During my doctoral studies, I worked with Prof. Sohail Murad (Illinois Institute of Technology) and Prof. Cynthia Jameson (University of Illinois at Chicago). We studied chiral drug separation processes on polysaccharide-based polymer surfaces. Besides the chiral drug separation, we also investigated fluid separations through zeolite membranes and provided insights when separations failed. I have also done molecular dynamics simulations that characterized the anisotropy of thermal conductivity at interfaces.

Proposed Research Areas

With my experience using multi-scale molecular simulations, along with knowledge of deep eutectic solvents, ionic liquids, zeolites, polymer materials and separation processes, I propose several research areas in which my expertise and knowledge will be leveraged.

1. To investigate zeolite synthesis in the media of ionic liquids and deep eutectic solvents by using various computational molecular modeling methods.

Synthetic zeolites, which constitute 2/3 of the global zeolite market and valued at 29.08 billion USD in 2016 with an expected annual growth of 2.5% during the period 2016 to 2022, remain one of the most popular materials and catalysts in the petroleum industry and in chemical engineering processes. Other than the traditional hydrothermal synthesis, ionothermal synthesis, using ionic liquids and deep eutectic solvents, avoids the use of water which is often problematic to recycle. Also, the IL can serve as both solvent and OSDA, which minimizes the competition between OSDA and solvent molecules. Another advantage of using ionothermal synthesis is the lack of vapor pressure of the solvent system, which allows the synthesis to be operated in open vessels and avoids high autogenous pressures.

Previous studies on the synthesis of zeolites and zeolitic materials using ILs and DESs are very limited, although aluminophosphate zeolitic analogues are more often mentioned. Given the superiority of ionothermal synthesis of zeolites over the hydrothermal method, various molecular modeling methods will be used to: understand the ionothermal synthesis mechanism at molecular level; find suitable ionic liquids and deep eutectic solvents in the ionothermal synthesis; promote discoveries of new zeolite and zeolitic porous materials. I believe my theoretical work based on molecular modeling will be able to provide lots of interesting insights and answers, which can help to provide new strategies to synthesis zeolites using this new type of solvent media.

2. Developing machine learning force fields for zeolite frameworks with the presence of OSDAs from DFT calculations

The shape and size of the cages, along with the aluminate anionic center distribution, are critical features for zeolite and zeolitic materials. Compared with expensive and complicated spectroscopy-based experiments to elucidate the nanoscale structure, molecular modeling simulation is often a more attractive (quicker and cheaper) alternative to understand molecular level behavior and predict zeolite structures. However, since no universal and accurate force field is available for such systems, many challenges still exist in applying various computational methods.

The main challenge, or perhaps obstacle, of applying molecular modeling methods in the study of zeolitic systems is the lack of reliable and universal force fields. With the advance in machine learning (ML) technology, accurate classical force fields now can be built through training short periods of AIMD simulations. In this proposed research area, by using the ML technology, I will develop a reliable force field which can be used to simulate and study zeolitic systems. By combining ML technology and molecular modeling, some tasks will be achieved and certain questions can be answered: provide accurate and inexpensive computational alternatives for simulating zeolites compared with ab initio modeling methods; enable the study of larger systems; facilitate the study of ionothermal synthesis of zeolites.

3. Investigating ion exchange in different zeolites

Since environmental issues and global warming are becoming increasingly problematic for society, researchers from academia and industry are exploring using batteries as an alternative to fossil fuels. Among energy storage technologies, the redox flow battery (RFB) has been identified as a potential solution, due to their safety, high capacity, efficiency and small environmental footprint. In the RFB, one of the key factors that influences the fuel cell performance is the choice of ion exchange membrane (IEM), because in a full electron flow circuit, protons have to transport across the IEM between the two electrode compartments.

Traditional research has been focused on using some organic membranes like sulfonated fluoropolymer-copolymers (commercially known as Nafion®). However, their low-efficiency and degradation over time still remain as limitations that inhibit the use of RFBs particularly those operating at elevated temperatures. Different from traditional polymer-based ion exchange membranes in redox flow batteries, zeolites will be investigated using molecular modeling methods, which will: provide understanding of ion transport behaviors through zeolite membranes; trigger discoveries of new membrane materials; make breakthroughs in designing redox flow batteries for energy storage applications. Moreover, since the solvent media is known to affect the transport behavior of ions, a wide variety of electrolytes other than water, including ionic liquids and deep eutectic solvents along with their different compositions can also be studied, which is expected to give a broader view of using zeolite as a membrane material. In such a way, this topic can also be tied to my proposed Research Areas I & II with the development of new zeolite materials.

Teaching Interests

Education is a lifelong experience with the roles of teacher and student repeatedly shifting. Choosing teaching as my permanent career will be one of the most satisfying decisions of my life. In my opinion, no profession other than teaching offers a greater potential to contribute to and positively impact society and the lives of all those involved in the learning process, including the instructor. I also strongly feel that learning is a two-way street, and both students and teachers learn a lot from each other during this process.

Looking back into my experience when I sat in the classroom, I found out that asking and answering questions between the lecturer and students is of vital importance for both roles. To this end, I plan to use problem-based learning techniques in my classes. It is an important job for a professor to pass his or her skills and knowledge to the next generation. So, I will also heavily involve graduate or undergraduate students in my research.

Given my background, I am confident of my ability to teach courses at the undergraduate or graduate level, especially courses in traditional chemical engineering, such as reaction engineering, thermodynamics and transport phenomena. I am also interested in developing courses in the area of statistical thermodynamics and computational modeling. Computational methods, including atomistic molecular modeling, machine learning technology and data science, have become important tools in chemical engineering especially in polymer design, bio-mechanics, etc. With my research experience, I can develop a project-based course on computational chemical engineering that can help students develop such skills. I will merge my expertise on computational molecular modeling, programing, scripting, machine learning and data science into the project-orientated course. Students are expected to benefit from the knowledge of latest computational modeling and its application in chemical engineering. Hence, such a course would not only be open to students of chemical engineering, but also to those from other departments, such as biology, physics, and computer engineering.