Towards the Accurate Simulation of Electrochemical Interfaces by Combining Electron Density and Long-Range Machine-Learning Methods
- TH Department Seminar
- Datum: 22.02.2024
- Uhrzeit: 14:00
- Vortragende(r): Dr. Andrea Grisafi
- Institute of Data and Computational Sciences, Sorbonne Université, Paris, France
- Ort: https://zoom.us/j/94537687996?pwd=ZGpvVFpQT1owM20xajJnUjF0SU5qUT09
- Raum: Meeting ID: 945 3768 7996 | Passcode: 693535
- Gastgeber: TH Department
![Towards the Accurate Simulation of Electrochemical Interfaces by Combining Electron Density and Long-Range Machine-Learning Methods Towards the Accurate Simulation of Electrochemical Interfaces by Combining Electron Density and Long-Range Machine-Learning Methods](/event_images/35680-1707748022.jpeg)
I will show how to tackle this problem by incorporating long-range structural information into an equivariant ML model capable of predicting the Kohn-Sham electron density of the system.1 I will continue by deriving a finite-field extension of the method, which allows us to reproduce the electronic charge transfer through the metal electrode, as generated by the application of an external electric field. Crucially, I will show how these developments can be integrated within the simulation of a gold/electrolyte ionic capacitor under an applied voltage, enabling the accurate prediction of the differential electric capacitance. I will conclude outlining how future applications of the method will make it possible to drive the large-scale simulation of electrochemical cells with the accuracy of finite-field density functional theory.
1Andrea Grisafi, Augustin Bussy, Mathieu Salanne and Rodolphe Vuilleumier, Predicting the charge density response in metal electrodes, Physical Review Materials 7, 125403 (2023).