Year: 2026
Type: preprint
Abstract: Materials discovery increasingly depends on a multiscale ecosystem of simulations, experiments, and shared facilities, yet advances in individual AI tools do not by themselves connect the decisions that link these components. This gap is especially severe in all-solid-state batteries (ASSBs), whose development couples electrochemical performance to interfacial stability, mechanics, processing, manufacturability, reliability, safety, and cost. Here we argue that AI agents could support ASSB development toward commercial viability by maintaining a shared decision–evidence state that links materials discovery and optimization to cell-level validation and manufacturing constraints. We develop this argument through three complementary dimensions of scientific agency: coordination across heterogeneous resources, method augmentation across models and scales, and project-level adaptation over time. Cross-domain demonstrations establish several relevant capability primitives, including stateful tool planning, task execution, and delegated analysis. Agents should therefore be evaluated as supporting infrastructure for concurrent ASSB engineering rather than as autonomous replacements for scientific expertise. Their value ultimately lies in driving materials development in the context of interfaces, cell architectures, and manufacturing processes toward the performance, reliability, safety, and cost requirements of ASSB commercialization. context & scale Commercializing all-solid-state batteries (ASSBs) requires more than identifying high-performing materials: ion transport, interphase chemistry, mechanics, processing, cell architecture, reliability, safety, and cost impose coupled constraints that can invalidate otherwise promising candidates. This Perspective argues that AI agents could serve as auditable infrastructure, maintaining a decision-evidence state that connects objectives, specimen histories, models, uncertainties, actions, and validation to support ASSB development. Such infrastructure could broaden research across instruments and engineering stages, strengthen validation by linking atomistic, mesoscale, continuum, and experimental descriptions, and preserve evidence and revisions throughout development cycles. Future efforts should move beyond task-level demonstrations toward systematic ASSB development, establishing whether agents can contribute to the development of reliable and economically viable ASSB systems.
Source: ChemRxiv
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225330753