In his presentation titled "Varying and connecting everything in battery experiments and modelling," Professor Stein will share his team's innovative approach to reducing battery research proliferation time from 20-30 years to just a few months. This is achieved by interleaving battery research from the lab bench to factories and back, using a new paradigm of research orchestration: fast intention agnostic learning via a brokering approach.

Stein's team employs intelligent robotic experimentation and manufacturing across the entire research value chain, elucidating how materials influence interfaces and cascade to battery packs, as well as how they propagate back. This paradigm shift necessitates good (degradation) models that perform well over a variety of designs, formats, chemistries, and electrochemical protocols.

The talk will also demonstrate how robotic experimentation can enable 
great accuracy for battery manufacturing in the lab and introduce an 
attention-based recurrent algorithm for neural analysis (ARCANA) of 
battery lifetime. By linking machine learning models, theory, and 
experiment into a materials acceleration platform that spans the entire battery research chain, their approach aims to achieve a 10-100x acceleration in battery research and development.

This year's OBMS symposium, held at Oxford's stunning Andrew Wiles 
Building, home of the Mathematical Institute, will feature prominent 
speakers from industry, academia, and national labs. The program includes an emphasis on continuum and systems modelling and a poster session showcasing cutting-edge research in battery technology.