Nonadiabatic Effects on Defect Diffusion in Silicon-Doped Nanographenes

Extracting Inelastic Scattering Cross Sections for Finite and Aperiodic Materials from Electronic Dynamics Simulations

Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. In this paper, it is demonstrated that electronic dynamics simulations can similarly be employed to study cross sections for the scattering-induced electronic excitations probed in nonresonant inelastic X-ray scattering and momentum-resolved electron energy loss spectroscopies. A method is put forth for evaluating the electronic dynamic structure factor, which involves the application of a momentum boost-type perturbation and transformation of the resulting reciprocal space density fluctuations into the frequency domain. Good agreement is first demonstrated between the dynamic structure factor extracted from these electronic dynamics simulations and the corresponding transition matrix elements from linear response theory. The method is then applied to some extended (quasi)one-dimensional systems, for which the wave vector becomes a good quantum number in the thermodynamic limit. Finally, the dispersion of many-body excitations in a series of hydrogen-terminated graphene flakes (and twisted bilayers thereof) is investigated to highlight the utility of the presented approach for capturing morphology-dependent effects in the inelastic scattering cross sections of nanostructured and/or noncrystalline materials.

Identifying and manipulating single atoms with scanning transmission electron microscopy

The manipulation of individual atoms has developed from visionary speculation into an established experimental science. Using focused electron irradiation in a scanning transmission electron microscope instead of a physical tip in a scanning probe microscope confers several benefits, including thermal stability of the manipulated structures, the ability to reach into bulk crystals, and the chemical identification of single atoms. However, energetic electron irradiation also presents unique challenges, with an inevitable possibility of irradiation damage. Understanding the underlying mechanisms will undoubtedly continue to play an important role to guide experiments. Great progress has been made in several materials including graphene, carbon nanotubes, and crystalline silicon in the eight years since the discovery of electron-beam manipulation, but the important challenges that remain will determine how far we can expect to progress in the near future.

Exploring electron beam induced atomic assembly via reinforcement learning in a molecular dynamics environment. NANOTECHNOLOGY 2021;33:115301. [PMID: 34768249 DOI: 10.1088/1361-6528/ac394a]

Atom-by-atom assembly of functional materials and devices is perceived as one of the ultimate targets of nanotechnology. Recently it has been shown that the beam of a scanning transmission electron microscope can be used for targeted manipulation of individual atoms. However, the process is highly dynamic in nature rendering control difficult. One possible solution is to instead train artificial agents to perform the atomic manipulation in an automated manner without need for human intervention. As a first step to realizing this goal, we explore how artificial agents can be trained for atomic manipulation in a simplified molecular dynamics environment of graphene with Si dopants, using reinforcement learning. We find that it is possible to engineer the reward function of the agent in such a way as to encourage formation of local clusters of dopants under different constraints. This study shows the potential for reinforcement learning in nanoscale fabrication, and crucially, that the dynamics learned by agents encode specific elements of important physics that can be learned.