Autonomous labs (SDLs)) are laboratories where the synthesis and characterization of materials are Directed by robots, with AI models intelligently selecting new conceptions of materials to be tested according to the previous experimental results. This ability Reflects the remarkable progress made in the fields of data analysis and AI, as well as the emergence of more affordable robotic technology. SDL have the potential of Revolutionizing research and development efforts linked to critical minerals and materials science.
The development and adoption of alternative and new materials are at the heart of American leadership in emerging technologies. CRITITIC MATERIALS are required to make a variety of advanced and efficient technologiesincluding wind turbines and aerospace and defense equipment. The progress of materials increases performance, reduce energy consumption, lower costs and lead to new paradigms for progress in clean energy technology, biochemistry and microelectronics. However, the question is whether the United States dedicates sufficient attention and political resources to obtain the advantage in the SDLs.
A key tool to advance research on materials
SDL allow researchers to explore drawings Quickly and optimize the new materials that they could not have considered otherwise. Research organizations, especially THE University of Liverpool,, Argonne National LaboratoryAnd Carnegie Mellon University begin to build SDLs. For example, Researchers from the University of Liverpool in 2020 used A mobile platform robot arm to synthesize and search for catalysts on 10 design parameters, finally director 688 experiences over eight days completely independently and identifying chemical formulations 6 times better than the basic line.
SDLs have several important advantages compared to current methods of experimentation in materials science. First, they can considerably improve the productivity of the labor of scientific companies, releasing highly qualified workers from the subordinate experimental workforce and allowing them to develop new theories or to distill new information from data collected independently. In addition, SDLs also give more reproducible results. Rather than a graduate student to read an article and try to reproduce the experience by hand, the procedures can be encoded and executed by the software stimulating the robot weapons.
Intensify the SDLs
American decision-makers should pay more attention to this emerging technological platform. At present, THE UNITED STATES does not have a clear financing policy or program for advance SDLS. While Lawrence Berkeley National Lab And Argonne National Laboratory have managed Internal and DOE funding And a few Researchers received NSF career grants related to their SDL work,, Total U.S. SDL expenses are less than $ 50 million and are not made in a direct programmatic manner. In comparison, Canada recently rewarded 200 million dollars at the Acceleration Consortium of the University of Toronto to develop SDLs, their largest research subsidy ever carried out.
Intensify the development of This strategic technology will require the authorization of the congress and the allowances directed from specific program offices in scientific financing agencies. These actions must be guided by A research strategy that deploys SDL platforms at selected National universities and laboratories For allow scientists and materials researchers better understanding How to integrate experimental automation into the often disorderly and stochastic process of the discovery of materials.
SDL Grand Challenge
In order to guarantee that this scientific funding gives real scientific information, this research funding must be associated by program with a large SDL challenge, where SDLs must demonstrate cutting -edge materials in chemicals and specific material applications in a defined time horizon. The May 2023 report on AI for Science, Energy and Security, released By a consortium of national laboratories of the Ministry of Energy (DOE), specifically highlights AI for autonomous discovery and contains a robust starting frame for such a research program.
This great SDL challenge would be similar in the concept the way the defense Advanced Research Project Agency (Darpa) 2004 Great challenge laid The Foundation For the U.S.The main position in autonomous cars. Darpathe innovative scientific financing arm of the Ministry of Defense, has created an autonomous autonomous vehicle test bed for teams to compete for pricing and advance the state–of–THE–art. This challenge Understood A price of $ 2 million and just under 8 million dollars in operating cost to operate, a good deal to lay the seeds for decades of progress and private investments in AI and automotive equipment for autonomous cars.
TIl SDL Grande Challenge Could in the same way Found the teams to execute SDL banks to advance the state of technology in various material fields And could be funded by the Advanced Research Project Agency for Energy (Arpa-E), the cousin of the DARPA in the Department of Energy. Just as the example of the United States in AI has led to a wealth of innovation in autonomous cars, the solid technical basis of the United States in advanced AI, in combination with the progress of robotics, can be used in a competitive flight flywife for the discovery and innovation of materials.
Charles Yang is an automatic learning engineer and former researcher at Lawrence Berkeley National Lab, working on autonomous laboratories. He is Currently an orise scholarship holder at the Ministry of Energy. Hideki Tomoshige is a research partner at the Renewing American Innovation Project at the Center for Strategic and International Studies in Washington, DC