Battery Tech – Leveraging Atomic Scale Modeling for Design and Discovery of Next-Generation Battery Materials
Rechargeable Li-ion batteries (LIBs) are revolutionizing electric vehicles and portable devices, but improvements are needed in areas such as power density, safety, reliability, and lifetime. Reliable atomic scale modeling enables rapid initial evaluation of large chemical and material design space, accelerating the development cycle of next-generation battery technologies.
Attend this webinar to learn about an advanced digital chemistry platform for developing next-generation battery materials with improved properties. The presentation will include use of physics-based and machine learning techniques for understanding structure-property relationships of different battery components. It will also outline an automated active learning framework for the development of neural network force fields to predict critical bulk properties of high-performance liquid electrolytes used in advanced batteries.
Attend this webinar and learn:
- Predictive capabilities of physics-based modeling for battery materials
- How automated high-throughput simulation workflows enable rapid screening of new material candidates
- How advanced neural network force fields can be applied for accurate electrolyte property prediction