Artificial intelligence is deeply integrated into laboratory workflows, ushering in a new era of research automation.

Release time:

2026-01-28 10:28

Recently, laboratory automation and intelligence have witnessed revolutionary progress. The deep integration of traditional automation equipment with artificial intelligence algorithms is giving rise to "intelligent laboratory systems" that can autonomously design experiments, execute them, and analyze results. These systems, by integrating robotic operation platforms, IoT sensor arrays, and machine-learning models, have achieved fully unmanned operations across the entire workflow—from sample preparation and reaction control to data acquisition.

Even more remarkable is that several cutting-edge research institutions have begun deploying AI experimental platforms equipped with autonomous research capabilities. These systems can independently search literature databases, design experimental protocols, and use robots to carry out high-throughput experiments—all based on pre-set research objectives. They also analyze results in real time to optimize subsequent experimental pathways. In the field of materials science, such systems have already succeeded in shortening the discovery cycle for new materials by as much as dozens of times. In drug development, AI-powered high-throughput screening platforms can perform millions of virtual compound tests every day. Industry experts predict that within the next three years, intelligent experimental systems with autonomous optimization capabilities will become standard equipment in research institutions, fundamentally transforming traditional research paradigms.