Many modern applications involve fast, far-from-equilibrium processes occurring in condensed phase, nanoscale, molecular and hybrid materials. Examples include solar cells, electronic, optoelectronic, spintronic and valleytronic devices, quantum information systems, batteries, detectors, memory devices, energetic materials, biomedical systems, etc. We developed a unique theoretical methodology, combining real-time density functional theory for the evolution of electrons with non-adiabatic molecular dynamics for atomic motions, and machine learning to accelerate the simulations and analyze results, allowing us to model non-equilibrium response in the time-domain and at the atomistic level, as it occurs in reality.
Envisioned by theorists in the 1980s, semiconductor quantum dots gave rise to nanotechnology. They exhibit atomic, molecular and bulk features, supporting multiple processes that compete and occur in parallel. Two-dimensional materials promise device miniaturization, and exhibit both semiconductor and metallic properties depending on excitation density. Plasmons in metallic structures create strong local fields and intense optical response, key features for optoelectronic devices. Metal halide perovskites possess organic, inorganic and even liquid properties. Drawing intense interest in both fundamental and applied research, their unique defect tolerance needs to be elucidated and replicated.
Understanding and controlling modern materials create multiple challenges due to qualitative differences between molecular and periodic, and organic and inorganic matter, and a diversity of processes, including charge and energy transport, excitonic, plasmonic and polaritonic excitations, charge-charge and charge-phonon scattering, quantum coherence, etc. Our simulations provide a unifying description of quantum dynamics on nanoscale, characterize timescales and branching ratios of competing processes, resolve debated issues, and generate theoretical guidelines for development of novel devices and applications.