Scale-aware self-driving laboratories for multiscale process development

Abstract

Self-driving laboratories (SDLs) can accelerate scientific discovery, but their broader value lies in supporting scale translation for multiscale process development. SDLs should generate scale-aware candidate landscapes that link laboratory-scale experimental results to representative-device and process-development stages, distinguishing candidate regions as validated for next-scale testing, or excluded by industrial constraints, or requiring targeted exploration to reduce scale-dependent uncertainty.