Advanced learning strategies for potential energy surfaces applied to organic electrolytes

• Applicants: Professor Dr. Christian Holm; Professor Dr. Johannes Kästner
• Subject Area: Physical Chemistry of Molecules, Liquids and Interfaces, Biophysical Chemistry
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 497249646

In this project we will develop efficient inter-atomic potentials for organic electrolytes via machine-learning techniques from accurate DFT data. Our workflow aims at optimizing each stage of the development process including data generation and selection, model training and refinement through active learning, as well as uncertainty estimation for accurate deployment in production simulations. The outcome of this project will include a fully optimized workflow for the development of arbitrary inter-atomic potentials for molecular modelling, including long range interactions, as well as high-performance software suites capable of implementing this workflow. Beyond methodological improvements, our project will facilitate the development of almost ab-initio accurate inter-atomic potentials for organic electrolytes and therefore, highly accurate MD simulation which should yield important insights into the structure and dynamics of complex molecular liquids at the nanoscale and beyond.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning