Regensburg 2022 – wissenschaftliches Programm

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SYNM: From Physics and Big Data to the Design of Novel Materials

SYNM 1: From Physics and Big Data to the Design of Novel Materials

SYNM 1.5: Hauptvortrag

Montag, 5. September 2022, 17:15–17:45, H1

Data-driven understanding of concentrated electrolytes — •Alpha Lee — Department of Physics, University of Cambridge, Cambridge, UK

Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials. Notwithstanding this, the structure and dynamics of concentrated electrolytes remain enigmatic. In this talk, I will show how machine learning can unravel hidden patterns in simulations of electrolytes, helping us understand the structure of concentrated electrolytes and mechanisms of ion transport. In the first part of my talk, I will illustrate how debates such as extent of "ion pairing" in concentrated electrolytes can be addressed using methods in unsupervised machine learning and Bayesian hypothesis testing. In the second part of my talk, I will discuss how machine learning help relate local ionic structure to molar ionic conductivity. This furnishes microscopic insights on what are the drivers of conductivity as a function of ion concentrations. More broadly, I will discuss the role that machine learning can play in not only delivering predictive models, but also serving as an "intuition pump" to understand complex soft matter systems.