SES AI (SES) Water Tower Research Fireside Chat Series summary

AI strategy overview

• Focus is on accelerating next-gen battery technology adoption using AI in science, manufacturing, and safety.

• AI is leveraged to speed up new material development, improve manufacturing quality, and enhance safety, especially for lithium metal batteries.

• Safety is the top concern for OEMs, with AI used to monitor battery health and predict incidents.

• AI for battery health monitoring has been in use since 2017, gaining importance with recent EV fire incidents.

• Urban air mobility (UAM) applications benefit from lightweight, high-energy batteries and AI-driven safety and certification.

Competitive advantages and data strategy

• Unique position comes from deep expertise in both battery technology and AI, with comprehensive data sets.

• Ability to simulate, synthesize, and validate molecules and battery chemistries in-house.

• High-quality data from lithium metal manufacturing and testing, with plans to expand to other chemistries.

• AI models are designed to be chemistry-agnostic, enabling cross-learning between lithium metal and lithium-ion data.

• Collaborations with national labs and companies are underway to scale data mapping from millions to billions of molecules.

AI applications and commercialization

• Three main AI applications: Science (electrolyte discovery), Manufacturing (defect detection), and Safety (field monitoring).

• AI for Science aims to map and analyze up to 100 billion small molecules for electrolyte innovation.

• Commercialization includes licensing new chemistries and formulations to battery makers and OEMs.

• AI for Manufacturing uses selective CT scan data to detect defects in every cell, reducing safety risks and costs.

• AI for Safety monitors batteries in the field, predicting incidents and improving warranty management.