About Sijia Dong
Prof. Dong is passionate about accelerating science using computation and automation. She received her PhD in Chemistry from California Institute of Technology in 2017, advised by Prof. William A. Goddard III, with whom and Dr. Ravinder Abrol she developed a first-principles-based and data-driven computational method to predict the structures of proteins that are crucial drug targets for many diseases. She carried out her postdoctoral research at the University of Minnesota with Prof. Donald G. Truhlar and Prof. Laura Gagliardi, and then at Argonne National Laboratory with Prof. Giulia Galli. Her postdoctoral work was to use and develop quantum chemical methods and workflows to study the photochemistry of molecules and materials in light-harvesting systems, and to use machine learning to accelerate quantum chemical methods.
The Dong Lab develops and applies physics-based and data-driven computational methods to understand multiscale processes and to develop design strategies for molecules, materials, and processes that matter in renewable energy, biomedicine, and other areas of societal importance. We combine quantum mechanics, statistical mechanics, machine learning, and applied mathematics 1) to understand the dynamical, electronic, optical, spin, and chemical properties and 2) to unravel the interplay between different time and length scales in complex chemical systems such as catalysis and light-matter interactions in realistic and complex environments.
Projects in the Dong Lab have components of computational methodology development, applying existing computational tools in both conventional and novel ways to study chemical and physical processes, and/or developing design strategies for molecules and materials.
Positions for graduate students and postdocs are available starting early 2021. There are also possibilities for undergraduate researchers. If you are interested in joining us, please email Prof. Dong for inquiries.
The Dong Lab develops and applies physics-based and data-driven computational methods to understand multiscale processes, from electronic structures to emergent properties. We use such understandings to develop design strategies for molecules, materials, and processes that matter in renewable energy, biomedicine, and other areas of societal importance.