Algorithm-driven, experiment without lights: Daina Technology’s "AI + Lights-out Laboratories" empowers scientific research to think for itself.

DynaTech

AI + LIGHTS – OUT LABORATORIES

In January 2025, *Nature* highlighted seven "tech trends to watch" for the year ahead: laboratory automation, urban ecosystem modeling, precision medicine, environmental restoration, and more. Then, in September 2025, China's Institute of Scientific and Technical Information released its report, *Disruptive Technology Foresight 2025*, featuring "40 disruptive technologies," among which was "the dark-lab—a fully automated, "unmanned" laboratory capable of seamlessly integrating R&D, experimentation, and production processes."

Breaking Boundaries: AI Reshapes the Scientific Research Decision Chain

The inherent dilemma of智能化 transformation in traditional laboratories stems fundamentally from the conflict between "data silos" and "decision-making delays." Daina Technology's independently developed AI Decision Hub addresses this challenge by building a cross-modal perception network that seamlessly integrates heterogeneous data—such as microscope images, environmental parameters, and equipment status—in real time, creating a closed-loop system of "experiment-data-decision." This groundbreaking innovation enables laboratories to achieve "autonomous thinking" for the first time: here, AI is no longer a passive tool but evolves into a "research partner" capable of dynamically adjusting parameters and optimizing experimental pathways based on scientific objectives.

Fusion: Data Assets Energize the Industry Ecosystem

The ultimate value of laboratory intelligence lies in transforming experimental processes into reusable digital assets. Daina Technology’s AI + Lights-out Laboratories leverage AI algorithms to achieve full-lifecycle management of experimental data—from sample collection to result analysis. Every step of the process ensures that data is encrypted, securely stored, and tamper-proof, creating a traceable "scientific digital footprint." This innovation not only boosts the efficiency of individual labs but also fosters an industry-wide data-sharing ecosystem: Through standardized interfaces, experimental parameters from different institutions can be accessed across platforms, turning even "failed experiments" into valuable industry knowledge.

AI + LIGHTS – OUT LABORATORIES

As AI permeates the entire chain—from experimental design and operational execution to data analysis—scientific research and production models are undergoing a fundamental transformation. The traditional linear process of "hypothesis-experiment-verification" is being replaced by a cyclical approach: "AI-generated hypotheses—robotic validation—data-driven model optimization." At Daina Technology, AI algorithms leverage reinforcement learning capabilities, enabling scientific research to shift from being "human-intensive" to "intellectually intensive." Even more profound is the impact of interdisciplinary integration: when real-time data from chemical experiments are combined with bio-simulation algorithms, or when material characterization results are directly fed into climate models, AI is dismantling disciplinary barriers and giving rise to a new paradigm known as "computational experimental science."

Daina Technology's innovative practices reveal a fundamental truth: the core of lab intelligence isn't about replacing humans, but rather expanding the dimensions of human cognition. When robotic arms operate with precision in the dark, and when algorithms unearth patterns from vast seas of data, researchers are freed from repetitive tasks, allowing them to channel their creativity toward truly groundbreaking scientific challenges. Meanwhile, Daina Technology’s newly established Black-Light Lab Innovation Center will integrate instrumented robots with an AI-driven scheduling platform, creating an "intelligent operating system" for the research field—a transformation that goes beyond mere technological advancement; it represents a radical reimagining of the future research ecosystem.