Machine Learning Models for Batteries - Tejs Vegge

Tejs Vegge (DTU) talks on uncertainty-aware maschine learning models for battery cells, chemical reaction networks and solid electrolyte interfaces. This is part of the Battery Interface Genome - Materials Acceleration Platform (BIG-MAP) project. They propose a radical paradigm shift in battery innovation, which will lead to a dramatic speed-up in the battery discovery and innovation time; reaching a 5-10 fold increase relative to the current rate of discovery within the next 5-10 years. BIG-MAP relies on the development of a unique R&D infrastructure and accelerated methodology that unites and integrates insights from leading experts, competences and data across the entire battery (discovery) value chain with Artificial Intelligence (AI), High Performance Computing (HPC), large-scale and high-throughput characterization and autonomous synthesis robotics. In short, BIG-MAP aims to reinvent the way we invent batteries and to develop core modules and Key Demonstrators of a Materials Acceleration Platform specifically designed for accelerated discovery of battery materials and interfaces.

[@GeladenBatteriepodcast]

Thank you for this great insights, amazing præsentation.

[@AjSmit1]

can't wait to see what they come up with two papers down the line! great video thanks