Surface-Specific Spectroscopy through Machine Learning

Activity: Talk or presentation types › Invited or keynote talk at national or international conference

Description

In this talk, I show how machine-learning predictions of the energies and forces of atomistic systems, along with predictions of their responses to applied electric fields, can be combined with imaginary time path-integral methods to provide fully quantum-mechanical predictions of surface-sensitive sum-frequency generation (SFG) spectra. These calculations require that the polarizations and polarizabilities of the systems be accurately predicted; the former in particular requires some thought about how to build models for bulk polarizations. For this, I describe two approaches that render the polarization straightforward to learn.
Period13 Sept 2024
Event titleComputational Molecular Science Meeting
Event typeConference
Degree of RecognitionNational