Scientific Machine Learning for Battery Research

This talk introduces key Machine Learning concepts with applications to battery research: Traditional Machine Learning: example applying symbolic regression to predict conductivity of Li‑ion battery electrolytes. Neural Networks: applications for predicting degradation trajectories of fast‑charged batteries. Explainable Artificial Intelligence: methods to interpret how neural networks classify infrared spectra for presence/absence of functional groups. Physics‑Informed Neural Networks (PINNs): hybridizing phsysical models and neural networks, with applications in materials science and electrochemistry. We argue for a critical assessment of AI beyond the hype: understanding AI’s strengths and limitations is essential to identify value‑generating applications that truly accelerate battery research.