(2co) Accelerated Energy Materials Discovery through Semiempirical Electronic Structure Methods

As Earth is predicted to approach a critical threshold of a 1.5-degree Celsius increase above preindustrial levels in the coming decade, the need to diminish dependence on conventional energy sources and address the urgent challenge of global warming becomes ever more important. Developing affordable renewable energy technologies, such as solar cells or hydrogen fuel, emerges as a pivotal solution. However, the discovery of novel materials poses significant challenges due to their vast chemical space, time-consuming experimental analysis, and computationally prohibitive theoretical investigations. Moreover, understanding the excited state properties of materials, which is crucial for effective solar cell materials design, often demands computational resources several orders of magnitude higher compared to ground state calculations.

My objective is to facilitate the discovery of energy materials, through accelerated virtual high-throughput screening and comprehensive understanding of the underlying physics. My research group will develop cost-effective electronic structure methods that synergistically combine semiempirical techniques and machine learning. Specifically, my group will be dedicated to reducing the cost of excited state property calculations, uncovering quantitative structure-property relationship, and identifying design principles for materials intended for energy applications. We will utilize machine learning techniques and iterative active learning loops to optimize semiempirical parameters and incorporate suitable approximations into complex quantum chemistry methods, enabling their practical application. To accomplish these goals, my research program will focus on the following aims.

• Developing semiempirical electronic structure methods that leverage machine learning for excited state properties of materials.
• Constructing a method-development workflow that automates data generation, assesses method performance, and enables method improvement through active learning.
• Designing of hybrid organic-inorganic materials for clean energy harvesting

Prior Work

With my extensive expertise in quantum chemistry method development, excited state simulations, automatic workflow construction, and virtual high-throughput screening, I am uniquely qualified to tackle the proposed research aims. During my doctoral research under the guidance of Prof. Timothy Berkelbach at Columbia University, I focused on advancing semiempirical electronic structure methods for nanomaterials, with an emphasis on transition metal dichalcogenides (TMDC) and hybrid organic-inorganic lead halide perovskites (HOIPs). In order to overcome the computational challenge of the GW-BSE method, a post-DFT excited state method, I developed semiempirical GW-BSE methods, revealing the physics underlying the optical properties of TMDC materials in response to the surrounding dielectrics, as well as investigating the behavior of exciton, trion, and biexciton of 2D HOIPs and the exciton fine structure of HOIP quantum dots. Furthermore, I successfully extended the application of my semiempirical GW-BSE methods to encompass arbitrary nanocrystals and large biomolecules,which provide a valuable tool for investigating excited state phenomena in optoelectronic devices utilizing quantum dots.

Expanding upon my graduate research, the primary focus of my postdoctoral research is to enhance the scalability of first principle simulations for various systems. As a postdoctoral researcher at MIT, working with Prof. Heather Kulik, I developed a methodology to assess the accuracy of the widely used density functional theory (DFT) and correct the DFT-specific errors focusing on the Metal-Organic Frameworks (MOFs). Additionally, I have been working on constructing an automatic workflow that streamlines the generation of DFT data sets for machine learning potential with an emphasis on minimizing DFT errors. I have actively engaged in collaborative endeavors with experimental groups, which include: understanding and designing the excited state properties of metal organic chalcogenides, investigating Cu/hBN surface catalysis for the methane to CO₂ reaction, and exploring gas sensing capabilities of conductive MOFs.

Teaching Interests

My teaching philosophy is to guide students in building their understanding from fundamental concepts and progressing toward complex phenomena, with the motivation stemming from their sense of continuous improvement. I am fully prepared to teach and develop courses in chemical engineering, chemistry, and physics, covering subjects such as thermodynamics and numerical methods. Additionally, I intend to build interdisciplinary course on computational materials modeling.

I have teaching experience as a teaching assistant, having taught undergraduate students in general chemistry and advanced physical chemistry. Moreover, as a postdoctoral researcher, I had the privilege of mentoring a senior undergraduate student’s thesis, serving as her advisor and a member of the thesis committee. This experience was truly fulfilling as I had the opportunity to design a research project tailored for a beginner, teach her essential computation skills, and guide her through the entire process from data analysis to thesis writing. With great enthusiasm, I aspire to establish and lead a research group, while teaching students and fostering their growth as I progress alongside them.