Self-driving labs (SDLs) revolutionize the combinatorial approach by combining robotics, artificial intelligence, and machine learning to automate and optimize experiments. A SDL is simply combining the best technologies of this time. This integration of best technologies promises to substantially accelerate the rate of materials discovery by 100 times, surpassing human capabilities alone.

In SDLs, data is collected through high-throughput experimentation which is fed into an AI system. The AI system learns from the results and predicts improved material candidates with desired properties for specific applications. Robotic platforms then autonomously test these predictions by synthesizing and evaluating the materials. The integration of data, computation and experiments is essential with each element guiding and refining the others in a feedback loop (figure 1). 

Traditional research methodologies are often centralized and lack comprehensive data sharing leading to redundant experiments. Research findings obtained at a small lab scale frequently fail to translate into practical applications, hindering progress. Consequently, billions of dollars are wasted on duplicative research efforts worldwide. Decentralizing research activities and open-sourcing data can address these challenges. In a decentralized network, there is no single point of failure (as shown in Figure 2). If one part encounters issues, others can continue functioning, maintaining overall research momentum. This redundancy ensures that scientific progress is not halted by localized problems. Open source allows material developers to modify, customize and build upon existing candidate designs. This flexibility enables researchers to experiment with new ideas without starting from scratch.  

Open-sourcing data is another critical component of this new paradigm. By sharing data freely, researchers can avoid redundant efforts and build upon each other’s work more effectively. High throughput labs generate large sets of data which can be analysed more efficiently through collaborative efforts across organizations. META AI exemplifies how open-sourcing data can revolutionize the pace of discovery. By providing a platform for sharing experimental data, META facilitates collaboration and accelerates innovation.

As Jason Hattrick Simpers, Professor at University of Toronto, puts it "The aim is not to run a 100,000 or billion experiments, but to perform the most informative experiments at any given time using concepts like active learning." 

SDLs represent a significant leap forward, combining AI, robotics, and high-throughput experimentation to rapidly discover and optimize new materials. Decentralization and open-sourcing of research data further enhance the potential of SDLs, fostering collaboration and ensuring that advancements can be built upon globally. 

While AI significantly speeds up the discovery and validation of new materials, the absence of a seamless method to scale these innovations from the lab to industrial production, hinders their practical application and limits the value of AI-based research. VSParticle offers a robust and scalable tool that bridges this critical gap by offering robust and scalable tool that can facilitate this transition from research to industrial production, ensuring that the promising materials identified through AI can be efficiently produced on a large scale.