(4cb) A Unified Theoretical Approach to Solving Challenges in Reaction Kinetics of Energy Materials

1. Research program overview and representative accomplishments.

My research spans multiple disciplines, including chemical engineering, chemistry, and data science. During my Ph.D., I specialized in chemistry, focusing on the development of novel carbon- and transition metal-based catalysts for various electrochemical reactions. These included oxidative and reductive reactions involving hydrogen (HER), oxygen (OER and ORR), nitrogen (NRR), and carbon dioxide (CO2RR). I conducted in-depth analyses of their thermodynamic and electronic properties at the atomic level. My work in electrocatalyst development has been published in esteemed journals such Appl. Catal. B (2020), Nano Energy (2019), and ACS Nano (2018). As a post-doctoral researcher at Texas A&M University, I broadened my expertise to encompass chemical engineering and data science. This expansion allowed me to apply diverse theoretical methodologies across different scales, enhancing my understanding of kinetic properties such as reaction rates, mechanisms, and surface interactions under varying conditions (t, T, P, and pH). I have also made significant contributions to the integration of theoretical methods, with publications in Chemical Engineering Journal (2024), ACS Catalysis (2023), and ACS Catalysis (2023). Further advancing my research, I have developed comprehensive methodologies that merge multiscale theoretical modeling, physics-based hybrid modeling, and data-driven machine learning, including transformer technologies. This innovative approach has not only advanced my field but also led to several first-author publications in renowned journals. My dedication to developing cutting-edge methods and energy materials continues to drive my research forward, contributing significant advancements to the catalysis field.

2. Past and ongoing research projects.

My research has focused on developing catalysts for the electrochemical catalysis, including HER, OER, ORR, CO2RR, and NRR. To achieve this, I have employed diverse theoretical methods such as density functional theory (DFT), kinetic Monte Carlo (kMC), and machine learning (ML). These methods have been integrated to simultaneously interpret thermodynamic, electronic, and kinetic properties, marking an innovative in the field. Specifically, key projects include:

2.1. Catalyst development using DFT method (Past project): Through DFT, I focused on understanding the thermodynamic and electronic properties of catalysts at the atomic level. This understanding is crucial for comprehending how catalysts interact with reactants, influencing both the activity and selectivity of catalytic reactions. Specifically, my previous research shed light on the behavior of catalytic sites by analyzing adsorbate binding energy, orbital distribution, and charge transfer. Further, the insights gained have significantly aided experimental groups in designing new catalysts, reducing their reliance on trial-and-error approaches. A notable work is featured in Nature Energy, 6 (6), 592-604.

2.2. Method development via DFT-kMC-ML integration (Ongoing project): Although DFT offers robust advantages, it is limited in its ability to analyze kinetic properties under realistic environments (t, T, P, and pH). To overcome this, I am integrating DFT with kMC simulation, further enhanced by ML techniques. This combination aims to concurrently assess thermodynamic, electronic, and kinetic properties across scales from atomic to mesoscale. This comprehensive approach provides vital information on the activity, selectivity, and operational lifetime of catalytic reaction, addressing the critical concerns of the electrochemical catalysis field. Notable works are featured in Nature Chemistry (2024) and Appl. Catal. B (2024) under review.

3. Future research plans with their potential impacts.

As previously outlined, my research aims to overcome the limitations of conventional methods by proposing new catalysts developed through innovative theoretical approaches, positioning me as a pioneer in this field. Based on this, my future research goals are clearly defined and focus on developing a method that allows for the accurate interpretation of catalyst properties and reactions under realistic conditions. Specifically, key projects include:

3.1. Utilization of physics-based hybrid modeling technique (Short term): Conventional first-principles models such as DFT and kMC are grounded in physics and do not rely on datasets, while ML method depends heavily on extensive dataset but lack a physical basis. This discrepancy hampers flexible analysis, as the reliance on either method biases the analysis of catalyst properties. Although ML is increasingly used in catalyst research, the application is often hindered by insufficient or low-quality data. To address this, I plan to utilize a hybrid modeling framework that integrates physics to mitigate the reliance of the ML models on high-quality data. This approach has not yet been applied to diverse electrochemical catalysis. By prioritizing this method, I expect to make significant advancements by proposing new methods to analyze changes in reaction mechanisms, surface configuration, and activity over time from a kinetic perspective, previously unachievable transformations with conventional DFT, kMC and ML methods.

3.2. Utilization of chemical language-based transformer model (Long term): While the hybrid modeling represents a significant advancement in integrating physics with ML, its ability to generalize beyond known data and specific domains remains limited. To transcend this barrier, my long-term research focus will shift towards harnessing transformer model, similar to those underlying advanced AI systems like ChatGPT. Transformers excel in recognizing complex patterns and dependencies in data, offering superior predictive capabilities that are not confined to the scope of existing datasets. Despite their proven success in fields such as natural language processing, where they interpret and generate human-like text, transformer models have yet to be applied to the analysis of electrochemical catalysis. This presents a unique opportunity to pioneer a method that could revolutionize how I predict and understand the dynamic behavior of catalysts under various conditions. Implementing transformer technology promises to enhance accuracy in modeling reaction mechanisms, providing deeper insights and more reliable predictions of catalyst dynamic behavior over time from both thermodynamic and kinetic perspectives. By adapting this cutting-edge technology to catalysis, I aim to open new avenues for discovering and developing catalytic materials and reactions, leveraging the transformative potential of AI to impact real-world chemical processes.

Teaching Interests

1. Teaching philosophy and experience.

The opportunity to mentor students is a significant motivator in my pursuit of a faculty position within academia. My previous teaching experiences, which I found deeply rewarding, underscore my belief that teaching is a powerful tool for fostering creativity and intellectual growth in students. I am convinced that a robust enthusiasm for learning and research is sparked by hands on experiences and natural curiosity. In my role as a faculty member, my goal is to foster a collaborative, student-centered environment where learning is a communal activity. This will be achieved through the use of interactive course materials and engaging activities. With over five years of experience in teaching multidisciplinary courses and honing my communication skills, I am well-prepared to support students in their collaborative learning endeavors and to guide them in expanding their research horizons by grounding them in a solid understanding of the subject matter. I also aim to motivate students by sharing insights from my professional journey, encouraging them to excel in their chosen fields, and nurturing a commitment to lifelong learning. During my Ph.D., I served as a teaching assistant (TA) for undergraduate courses in General Chemistry, Physical Chemistry, and Quantum Chemistry. My responsibilities included conducting weekly office hours, preparing and grading assignments and exams, and occasionally lecturing large classes in the absence of the advisor. Additionally, as a teaching fellow (TF) in Computational Materials Sciences, I instructed students on the use of a web-based interface to computational chemistry package (WebMO), and taught them to analyze the geometric and electronic properties of molecular structures. These roles as a TA and TF were both exciting and rewarding experience, particularly when I watched students overcoming initial challenges. They also equipped me with valuable skills in student management, course organization, and effective communication. Throughout my research career, I have supervised five Ph.D., three M.S., and two undergraduate students in projects related to energy materials for electrocatalysis and battery system, employing various theoretical methodologies. My role involved frequent discussions and providing directional advice on their research. Such dynamic interactions will expose students to diverse viewpoints, enhancing both their communication and critical thinking skills, which will be crucial as I establish and lead my own lab.

2. Teaching interest.

I am well prepared to teach courses spanning Chemical Engineering and Chemistry, as well as energy materials, nanotechnology, computational science and engineering in general. My academic background includes successful completion of courses such as Thermodynamics, General Chemistry, Physical Chemistry, Quantum Chemistry, Bionano Technology, and Bionano Engineering during my B.S. and Ph.D. Based on this solid educational foundation, I believe that I can cover diverse engineering courses including, but not limited to, Chemical Engineering Thermodynamics, Chemical Engineering Materials, Chmiecal Engineering Kinetics, and Reaction Engineering. Additionally, I am eager to develop and introduce two new graduate-level courses: “Energy Material Design and Application”, will explore the design, functionality, and application of energy materials, integrating principles from geometry, electronics, chemistry, and mechanics. The second course, “Computational Materials Sciences”, will cover a range of simulation methods from the atomic to the mesoscale, discussing their underlying principles, processes, and specific applications across various systems. Furthermore, I am open to developing other classes that would enhance the course portfolio of the department. In all these endeavors, my extensive academic/research experiences will allow me to provide students with unique perspectives and examples to expand their knowledge.