The discovery of new materials is a crucial driver for progress across various industries. However, it’s surprising that even today, much of the research and development of new materials still relies heavily on human labor, with experimental data sometimes recorded on paper.
Developing new materials involves countless combinations of variables and trial-and-error attempts. Relying solely on human effort to find solutions among millions of possibilities is bound to be time-consuming, costly, and full of obstacles. Another often-overlooked barrier is the limitations of human cognition. Scientists may be confined by their own biases or limited knowledge, searching only within familiar territories and missing other possibilities.
These challenges, coupled with increasing development complexity, have caused the pace of new material discovery to lag behind industrial advancement, resulting in a phenomenon known as the “anti-Moore’s law.” Fortunately, the integration of AI and robotics offers a way to overcome this issue.
One standout in this field is the U.S.-based startup Atinary. They combine AI, machine learning, robotics and automation to create Self-Driving Labs—automated labs that leverage streamlined processes, data-driven decision-making, and intelligent experimental design. This approach not only allows more experiments to be conducted in less time but also reduces human errors and biases, leading to greater accuracy.
Self-Driving Labs are like time machines for scientists, capable of accelerating the discovery and R&D of materials by an average of 10 to 100 times. Take, for example, the optimization of a catalyst for converting CO2 into methanol. According to a study by ETH Zurich, Atinary’s algorithm achieved in just six weeks what previously took traditional research a century—speeding up the experimental process by a thousand times.
Moreover, Atinary stands out for its easy-to-adopt no-code machine learning platform. After just two hours of training, lab personnel can start deploying machine learning and set up their own Self-Driving Labs without writing complex code.
Japan’s largest pharmaceutical company, Takeda, partnered with Atinary to accelerate its drug development process by integrating AI-driven Self-Driving Lab technology. Since the collaboration began, Atinary has successfully helped Takeda significantly improve the effectiveness of its experiments, maximizing yields from under 50% to above 90%.
Beyond Takeda, major international institutions like IBM Research, MIT, and Snapdragon Chemistry have also partnered with Atinary, seeing it as their secret weapon to speed up R&D by hundreds of times.
The increased efficiency in developing new materials can drive industrial innovation forward. For drug discovery, the impact is even more profound, as every moment saved could mean more patients being cured.
Can’t wait to see AI-driven Self-Driving Lab technology become more widespread in other time-critical fields, becoming the ultimate time machine for human innovation!
