Adoption context · Chemistry · Materials science
Review sets criteria for atomistic foundation models in chemistry and materials
Review sets criteria for atomistic foundation models in chemistry and materials: context signal for AI-enabled research infrastructure, adoption, policy, or field diffusion.
Summary
The Nature Reviews Chemistry perspective reviews machine-learned interatomic potentials as possible foundation models for atomistic simulation. It discusses data scaling, architectures, evaluation, deployment and criteria for models that could be efficient, transferable and robust enough for chemistry and materials research.
AI role
AI is the object of institutional, policy, funding, adoption, or field-level measurement across chemistry, materials science.
Narrative role
This is context evidence: it shows the institutional, data, compute, infrastructure, policy, or diffusion conditions around AI-enabled science.
Caveat
This is an enabling, adoption, or governance signal, not direct evidence of accelerated discovery.