The Dong Lab develops and applies physics-based and data-driven computational methods to understand multiscale processes, from electronic structures to emergent and macroscopic properties. Based on this understanding, we 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 matrices. When existing methods cannot help us reach our scientific goals, we develop new methods.

We are curiosity-driven and not limited by traditional boundaries of science. 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. Techniques involved include electronic structure theory, semiclassical methods, molecular dynamics, machine learning, mathematical modeling, and possibly others.

Scientific topics we are currently interested in include

  • Multiscale processes and emergent properties in materials and biological systems

  • Enzyme, biomimetic, and heterogeneous catalysis

  • Light-controlled chemical and physical transformations in materials and biological systems

  • Molecular engineering for renewable energy, biomedicine, and beyond

  • Automated science