From Multiscale Emergence to Testable Intervention

Abstract

Precision chemistry requires more than accurate prediction: it requires a traceable chain from measurement to representation, mechanism, design, and reproducible intervention. We argue that, within a declared operating envelope, a small set of physically meaningful coordinates and explicit relations can reduce multiscale prediction and design to a closed model that can be evaluated within specified error and computational budgets. We formulate five requirements for such coordinates─predictive sufficiency, dynamical closure, interventional stability, identifiability, and reachability─and organize them in a representation–closure–search–intervention (RCSI) cycle. Its central object is an input–output description mediated by physical variables whose relations can be examined and revised. Analytic, numerical, learned, and hybrid solvers are selected according to accuracy, cost, and latency. Uncertainty and provenance are retained at every stage, and models are evaluated by extrapolation, mechanism discrimination, and intervention success. Examples from catalytic interfaces, electronic and atomistic models, polymer field theory, microscopy, active matter, biomolecular design, and quantum-control planning illustrate both the opportunity and the limits. We conclude with prospective tests that can falsify each stage and a reporting standard for closed-loop scientific AI.