Combined QM-ML Approach to Accelerate Photodynamics Simulation with High Robustness

Pin-Han Chen*, Javier Carmona-Garcia, Lambert Schomaker, Daniel Roca-Sanjuán

*Corresponding author for this work

Research output: Contribution to conferencePosterAcademic

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Abstract

The main purpose of this work is to develop a robust approach to accelerate QM
photodynamics simulation. DFT calculation is conducted to provided a standard of the predicted properties for each molecular geometry. CNNs are applied to the descriptors to predict energies and forces for all states rapidly. Active learning is coordinated with the simulation itself, which guarantees that the underexplored regions are calculated with QM, and the model covers the region where the trajectories are reaching.

Conclusions:
* With the QM-ML workflow,QM photodynamics is accelerated 14.5 times

* Ensemble LOL + SchNet predict energy and forces more accurately and is
more reliable on querying undersampled points

* Incorporating active learning with the simulation enhances the robustness
of the workflow

* Results of QM-ML + ZNSH simulation agree well with QM + FSSH

* More trajectories with longer time lengths can be simulated
Original languageEnglish
Number of pages1
Publication statusPublished - 21-Jun-2022
Event12th Congress on Electronic Structure Principles and Applications (ESPA-2022) - Valencia, Spain
Duration: 21-Jun-202224-Jun-2022
Conference number: 12
https://espa2022.webs.uvigo.es/welcome/

Conference

Conference12th Congress on Electronic Structure Principles and Applications (ESPA-2022)
Abbreviated titleESPA2022
Country/TerritorySpain
CityValencia
Period21/06/202224/06/2022
Internet address

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