Breakfast with Networking – Sponsored by YTC AMERICA

STANFORD (Tutorial A): Advances in Electrolyte and Additive Design for Next-Generation Lithium Metal Batteries

This tutorial will highlight recent advances in electrolyte engineering for high-energy lithium metal batteries, including our group’s latest
developments in solvent design. I will also discuss complementary additive strategies that enhance interphase stability, improve Coulombic
efficiency, and extend cycling life. Attendees will gain an integrated perspective on electrolyte–electrode interfacial control.

(Tutorial B): Beyond Graphite – Next-Generation Anode Materials for Lithium-ion Batteries

Graphite is a low-cost, abundant, low-voltage, high-capacity anode material for lithium-ion batteries. It is straightforward to understand why graphite dominates the anode market. The first question we will address then is ‘Why?’ look beyond graphite. From there, we will investigate the materials that provide functionality—energy density, power/charge rate, lifetime, safety—that cannot be met by graphite.

JOHNS HOPKINS (Tutorial C): Tracking Cathode Degradation in Real Time Using Electrochemical Impedance Spectroscopy (EIS), Dynamic EIS, and Distribution of Relaxation Times (DRT)

Cathode degradation often begins as subtle impedance growth before capacity fade is obvious. This tutorial shows how to diagnose degradation mechanisms using Electrochemical Impedance Spectroscopy (EIS), including dynamic EIS collected during battery cycling, and interpret overlapping processes with the Distribution of Relaxation Times (DRT), which converts frequency data into a distribution of time constants. We will also highlight emerging joint-domain (hybrid) impedance spectroscopy for solid-state batteries (SSBs), which combines time- and frequency-domain data to enable much faster measurements.

UNIVERSITY OF MICHIGAN (Tutorial D): Insights from Monitoring Battery Expansion During the Formation Cycle

This talk will explore the correlation among the formation protocol, the first-cycle coulombic efficiency, the first-cycle irreversible battery expansion, and cycle life. We show that monitoring changes in battery thickness can provide key insights into this critical last stage of manufacturing.

(Tutorial E): Integrating Physics-Based Simulations and Machine Learning to Fast-Track Battery Materials Innovation

Developing next-generation batteries requires deep insight into complex phenomena like ion transport and the SEI. Integrated physics-based modeling and machine learning approaches are revolutionizing the development of next-generation battery chemistries. We demonstrate how ML Force Fields and advanced ML models can rapidly predict material properties, significantly reducing R&D timelines for high-performance energy storage systems.

(Tutorial F): TBA