Artificial intelligence is increasingly capable of predicting chemical properties

generating candidate structures

and assisting experimental planning. Yet the rate-limiting step in energy and chemical innovation is no longer prediction alone: it is the conversion of computational proposals into reproducible experiments and deployable process decisions. In this Perspective

we argue that Artificial Intelligence for Science in chemistry is undergoing a decisive transition from model-centric performance improvement to executable

closed-loop research infrastructure. This transition involves three coupled layers. First

physically grounded models must connect molecular and materials structures with energetics

kinetics

experimental observations

and uncertainty. Second

design algorithms must operate in validation-aware workflows that link inverse design

mechanistic computation

experimentation

and process constraints. Third

autonomous laboratories require reusable agent infrastructure that integrates scientific software

instruments

analytical feedback

safety control

and human accountability. We discuss developments in molecular and catalyst design

reaction optimization

digital twins

and self-driving laboratories

and identify data provenance

physical execution

reliability benchmarking

and safety governance as central translational challenges. For energy and chemical engineering

the value of artificial intelligence will ultimately be determined not by the number of candidates it proposes

but by its ability to close the loop from hypothesis to validated technology.