This work presents a template-based platform that separates workflow design from execution in self-driving laboratories and shows promise in translating scientific intent into executable automated workflows.
Abstract
Self-driving laboratories (SDLs) promise to accelerate experimental research, yet translating scientific intent into executable automated workflows remains a bottleneck. We present a template-based platform that separates workflow design from execution....
Lab automation is a key enabler of algorithmic-driven research in materials science, by increasing the throughput and accelerating experimental workflows researchers can generate the large amounts of experimental datasets required...
Pablo Quijano Velasco, Chang Jie Leong, E. H. H. Koay et al.· Digital Discovery· 0 citations
Self-driving laboratories (SDLs) integrate robotic experimentation and machine learning to autonomously navigate complex chemical and materials search spaces-but their development demands simultaneous mastery of experimental parameterisation, automated measurement, and optimisation...
Veronica Lai, Erin Lindenberg, Jason E. Hein et al.· Digital Discovery· 0 citations
Self-driving laboratories (SDLs) are rapidly changing how chemical and materials research is performed by closing the loop between automated experimentation and data-driven decision making, enabling faster discovery and optimization with...
S. Sadeghi, Jin-Ge Xu, Kristofer G. Reyes et al.· Digital Discovery· 0 citations
