Machine learning systems under the influence of an external bias: introducing potentiostat models in neural network molecular dynamics simulations

• Grant number: 23/11751-9
• Support Opportunities: Scholarships abroad - Research
• Start date: January 01, 2024
• End date: December 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Alexandre Reily Rocha
• Grantee: Alexandre Reily Rocha
• Host Investigator: Mariana Rossi Carvalho
• Host Institution: Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
• Institution abroad: Max Planck Society, Hamburg, Germany
• Associated research grant: 21/14335-0 - ICTP South American Institute for Fundamental Research: a regional center for Theoretical Physics, AP.ESP

Abstract

Understanding the reactions and structures at the water/metal interface plays a key role in electrochemical energy conversion and storage. However, providing a theoretical and experimental description at molecular scale of electrified interfaces under electrochemical conditions remains a challenge. First-principles simulations have provided new perspectives and crucial insights about the structure of water at the metallic interfaces. Nevertheless, realistic computer simulations of electrochemical cells that include solvent effects and explicitly describe aqueous electrolytes under an external bias potential are still computationally very expensive for the system sizes and timescales needed to reliably study electrochemical processes. In this project, we will implement a methodology for a potentiostat within the canonical ensemble, and combine it with different machine learning frameworks to speed up calculations, and allow us to treat large systems. The resulting code will lead to a greater understanding of water/metal interfaces as we will be able to simulate the effects of an external bias for large system sizes and long timescales, i,e., a realistic electrochemical cell. (AU)