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AI3SD Video: Machine learning for electronically excited states of molecules

AI3SD Video: Machine learning for electronically excited states of molecules
AI3SD Video: Machine learning for electronically excited states of molecules
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].
[1] J. Westermayr, P. Marquetand, “Machine learning and excited-state molecular dynamics” Chem. Rev., in press, doi:10.1021/acs.chemrev.0c00749 (2020).
[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).
AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphys, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks
Westermayr, Julia
339a6e66-8d94-4900-9786-5ef00939cddb
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
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Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84
Westermayr, Julia
339a6e66-8d94-4900-9786-5ef00939cddb
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Hooper, Victoria
af1a99f1-7848-4d5c-a4b5-615888838d84

Westermayr, Julia (2021) AI3SD Video: Machine learning for electronically excited states of molecules. Kanza, Samantha, Frey, Jeremy G., Niranjan, Mahesan and Hooper, Victoria (eds.) AI3SD Winter Seminar Series, , Online. 18 Nov 2020 - 21 Apr 2021 . (doi:10.5258/SOTON/P0080).

Record type: Conference or Workshop Item (Other)

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].
[1] J. Westermayr, P. Marquetand, “Machine learning and excited-state molecular dynamics” Chem. Rev., in press, doi:10.1021/acs.chemrev.0c00749 (2020).
[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).

Video
AI3SD-Winter-Seminar-Series-GNM-JuliaWestermayr - Version of Record
Available under License Creative Commons Attribution.
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More information

Published date: 3 February 2021
Additional Information: 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.
Venue - Dates: AI3SD Winter Seminar Series, , Online, 2020-11-18 - 2021-04-21
Keywords: AI, AI3SD Event, Artificial Intelligence, Chemistry, Graphys, Machine Intelligence, Machine Learning, ML, Molecules Discovery, Networks

Identifiers

Local EPrints ID: 448777
URI: http://eprints.soton.ac.uk/id/eprint/448777
PURE UUID: 74f4b4ee-ad62-4cd9-87f8-7285de223498
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 05 May 2021 16:41
Last modified: 22 Oct 2021 01:55

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Contributors

Author: Julia Westermayr
Editor: Samantha Kanza ORCID iD
Editor: Jeremy G. Frey ORCID iD
Editor: Mahesan Niranjan
Editor: Victoria Hooper

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