Artificial intelligence is becoming central to the generation and dissemination of information in all fields, and countries are all looking for the next AI asset that will give them an advantage.
Now imagine a world where the tedious and time-consuming tasks of scientific research are handled not by humans, but by sophisticated machines. That’s the era of the self-driving lab, a revolutionary concept that’s transforming the landscape of research and development.
A self-driving lab is an automated platform that integrates artificial intelligence (AI), robotics, and digital technologies to conduct experiments with minimal human intervention. These labs represent the cutting edge of scientific discovery, where AI algorithms design experiments, robots carry out the procedures, and machine learning models analyze the results, iterating this process to optimize outcomes and accelerate innovation.
The concept of a self-driving lab is akin to the self-driving cars that navigate roads using sensors and complex algorithms. In the laboratory setting, robots act as the hands that carry out the experiments, while AI serves as the brain, making decisions based on data. This synergy between robotics and AI allows for a level of precision, efficiency, and reproducibility that is challenging to achieve in traditional—that is, human controlled– research environments.
One of the primary goals of self-driving labs is to automate the scientific process. By doing so, they can run hundreds of experiments simultaneously, evaluate results, identify patterns, and determine the next steps without human bias or error.
Experts say that currently it takes 20 years and $100 million on average to go from the discovery of a new material to high-volume advanced manufacturing of it. Self-driving labs could accelerate the process 100 to 1,000 times, potentially bringing a 10-plus-year operation down to less than a few months and cutting the cost from $100 million to less than $1 million, says Milad Abolhasani, an associate professor at North Carolina State University who works on the technology.
The implications of self-driving labs are vast. In the chemical industry, for instance, they offer a chance to reshape research and development, accelerating the pipeline and time-to-market for new products. In materials science, self-driving labs are being used to rapidly explore and optimize new materials, some of which could lead to advancements in clean energy technology, biochemistry, and microelectronics.
However, as Sujai Shivakumar, a senior fellow at the Center for Strategic and International Studies, points out that there are currently very few countries that have all three: AI, robotics and advanced computing capabilities, adding that “the U.S. needs to step up and capture the benefits of our research.” Leading research institutions like Argonne National Laboratory and the University of Toronto are at the forefront of this autonomous discovery movement. The Canadian government has invested $200 million in the Toronto project. The U.K. government is funding the Materials Innovation Factory, an autonomous lab collaboration between the University of Liverpool and Unilever.
Self-driving labs are still not on the near-horizon; they face engineering and hardware hurdles, what Abolhasani calls “real chemistry.” There also isn’t standardized hardware and software, and there is nowhere near the amount of AI-training data that is available for chatbots.
But what about the human factor and what is the future of scientific careers and the skills that will be in demand? As the field evolves, there will be a growing need for professionals who can design, manage, and interpret the work of these automated systems. Educational institutions and research organizations are already beginning to prepare the next generation of scientists for this new landscape, with internship programs and courses focused on robotics, AI, and machine learning. Rather than taking away jobs from humans, self-driven labs will create new opportunities.
