Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session T46: Excited State V: real time TDDFT
11:30 AM–2:30 PM,
Thursday, March 17, 2022
Room: McCormick Place W-470A
Sponsoring
Units:
DCOMP DMP
Chair: Yuan Ping, UC Santa Cruz
Abstract: T46.00006 : First-Principles Methods for Simulating Inelastic Scattering of High Energy Electron Beams by Materials*
12:54 PM–1:06 PM
Presenter:
David B Lingerfelt
(Oak Ridge National Lab)
Authors:
David B Lingerfelt
(Oak Ridge National Lab)
Jacek Jakowski
(Oak Ridge National Lab)
Panchapakesan Ganesh
(Oak Ridge National Lab)
Bobby G Sumpter
(Oak Ridge National Lab)
Recent work relying on the convergent electron beams found in aberration-corrected scanning transmission electron microscopes (STEMs) has demonstrated that highly localized chemical reactions can be controllably induced in materials via electron irradiation. In certain cases, electron beam-induced reactions can be promoted with true atomic precision. Such reactions are understood to be driven largely by elastic electron-nucleus scattering processes, through which beam electrons can transfer significant linear momentum to individual nuclei. However, inelastic scattering —which results in coupled electronic and vibrational excitations in the material — can also occur simultaneously with the elastic scattering by the nuclei. Whether changes in the potential energy landscape of materials due to such excitations nontrivially influence the reaction kinetics remains an open question. Our group has recently developed a suite of first-principles tools capable of simulating the electronic and vibrational response of materials to electron beams, both in real- and momentum-space. The methods are first overviewed, and their application to some simple systems of experimental interest is then presented. Finally, we provide some perspective on the use of these simulations to guide experimental efforts in the area of beam-induced materials chemistries, and how machine learning approaches can provide a bridge between the disparate timescales of these STEM experiments and ab initio simulations.
*This work was performed at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science user facility, and used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
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