From hype to impact: A roadmap for trustworthy battery AI

Abstract

Artificial intelligence is increasingly used across the battery lifecycle, including materials screening, manufacturing quality control, diagnostics, and second-life assessment, yet its real-world impact remains limited by fragmented data, constrained interpretability, and the absence of deployment-ready governance. This Commentary proposes a roadmap for trustworthy, field-ready battery AI shaped by three structural priorities. First, open and standardized data ecosystems, supported by interoperable metadata and benchmark tasks, are essential for overcoming heterogeneous and siloed datasets. Second, privacy-preserving industrial collaboration can be enabled through federated learning, encrypted inference, synthetic data, and auditable governance frameworks aligned with safety-critical expectations. Third, physically grounded and interpretable models that embed electrochemical priors, enforce physical constraints, and quantify uncertainty are required to ensure robustness across chemistries, formats, and operating regimes. This roadmap reframes battery AI from isolated performance gains toward trustworthy, system-level intelligence capable of delivering sustained scientific and industrial impact.