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Theory and Modeling

We use a variety of levels of theory, including electronic structure, molecular dynamics, electrodynamics and quantum dynamics, as well as machine learning and artificial intelligence approaches, to understand and predict nanoscale phenomena.

We apply and develop a broad range of state-of-the-art theoretical approaches for predicting the electronic structure, optoelectronic and dynamical properties of nanoscale materials. Additionally, we have expertise in machine learning or, more generally, artificial intelligence approaches for inverse design, force field development and the analysis of the large amounts of experimental data that arise from multimodal and ultrafast measurements.

Theory and modeling are essential for advancing nanoscience, not just in terms of providing explanations of experimental results, but also for leading to new experiments and directions. The methodology and capabilities that the Theory and Modeling group have, and are developing, are applied to scientific problems in the Center for Nanoscale Materials’ three themes that the group is particularly interested in. These include, but are not limited to:

  • Quantum materials and sensing: probing entanglement dynamics, photon generation and mechanical cooling in hybrid quantum systems, quantum dot or, more generally, qubit interactions; using simulation to suggest new means for error mitigation in quantum systems.
  • Manipulating nanoscale interactions: the control of mechanical energy dissipation at the nanoscale; learning how to manipulate light interactions in structured nanoscale arrays to achieve desirable outcomes such as flat lensing.
  • Nanoscale dynamics: modeling and understanding of electron-induced heat for nanoscale thermal management and generation of non-equilibrium phase diagrams to guide the synthesis of metastable materials; incorporating first-principles data and experimental imaging data to yield precise atomistic-scale structures for real-time dynamics and other applications.


  • Carbon, a High Performance Computing Cluster (30 teraflops)
  • Electronic structure, molecular dynamics and electrodynamics codes
  • Quantum dynamics and cavity quantum dynamics codes
  • Machine learning based toolkits for force field development and atomistic imaging from multimodal data

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