AI3SD Winter Seminar #5: Graphs, Networks & Molecules: Talk 1 - Dr Julia Westermayr

This video forms part of the AI3SD Winter Seminar Series 20/2021.

This video is the first talk in the fifth seminar of the series: Graphs, Networks & Molecules.

Machine learning for electronically excited states of molecules – Dr Julia Westermayr

Abstract: An accurate simulation of the excited states of molecules can enable the study of many important processes that are fundamental to nature and the life forms we know, but these calculations are seriously limited by the high complexity and computational efforts involved. In this talk, I will discuss how machine learning algorithms can enable an efficient and accurate computation of photo-initiated reactions of molecules – from light excitation to nonradiative decay [1]. On the example of the methylenimmonium cation, I will introduce the SchNarc approach [2] and demonstrate the accuracy of its machine-learned potentials via UV/visible absorption spectra and nonadiabatic dynamics simulations [2,3]. Better statistics and long time-scale dynamics simulations become accessible with SchNarc, which would not be feasible without the help of ML [2-4].
[2] J. Westermayr, M. Gastegger, P. Marquetand, “Combining SchNet and SHARC: The SchNarc machine learning approach for Excited-State Dynamics”, J. Phys. Chem. Lett. 11(10), 3828-3834 (2020).
[3] J. Westermayr, P. Marquetand, “Deep learning for UV absorption spectra with SchNarc: First steps towards transferability in chemical compound space”, accepted in J. Chem. Phys. (2020).
[4] J. Westermayr, M. Gastegger, M. Menger, S. Mai, L. González, P. Marquetand, “Machine learning enables long time scale molecular photodynamics simulations”, Chem. Sci. 10, 8100-8107 (2019).

Bio: I am a postdoctoral research fellow developing machine learning models to study photoplasmonic catalysis since Oct. 2020. My main goal is to enable computationally efficient and accurate nonadiabatic dynamics simulations by decoupling the costs of accurate quantum chemistry calculations from the dynamics simulations. Therefore, I aim to fit potential energy surfaces, forces, and related properties (dipole moments, nonadiabatic coupling vectors, electronic friction tensors,…) of molecules and materials based on first principle reference data.

This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1