12-03, 16:30–17:00 (UTC), Talk Track I
The electrochemical battery is one of the most important technologies for a renewable future. In this beginner-friendly talk, we will walk through how fundamental quantum mechanics and data science inform how we fine-tune battery materials for higher performance. We will also show how we used these techniques to computationally model a lithium-oxygen battery in Python.
To design better batteries we need to understand the stuff – or matter – they are made out of. But how does one gather understanding of matter? To put it simply, by understanding its electronic structure at the fundamental quantum level. In this talk we will talk about the field of computational materials design for batteries from scratch (no knowledge of physics required!).
This talk will cover:
– Description of the electronic problem – the fundamental building block we must understand
– Brief introduction of density functional theory (DFT) – the model we use to learn fundamental quantum mechanical phenomena that are relevant for battery materials design
– How Python and data science can be used to create DFT programs
– Toy example of modelling a battery
– How machine learning is changing DFT and battery research
No previous knowledge expected
I am a computational scientist fascinated with the physics of matter and high energy systems. I have previously researched computational methods to solve the equations of plasma physics – as well as ways to compute battery parameters from quantum mechanical codes.