Humans are still needed to interpret results, and this can be open to controversy, like in the case of the A-Lab project. The machines must also be told by a human where to look and what project to focus on, and currently lack the general exploratory powers of a human researcher

Self-driving labs can perform experiments thousands of times faster than a human and they don’t need to sleep. That means more science in less time, but many questions remain, says Alex Wilkins

Self-driving labs (SDLs) are a technology that uses artificial intelligence (AI) and robotics to develop new materials. SDLs combine AI with automated robotic platforms to autonomously discover new materials. 

SDLs are a strategy for accelerating materials and chemical research. They combine automated experiments with AI to direct them. SDLs incorporate machine learning (ML) to intelligently explore the chemical space. 

The Acceleration Consortium at the University of Toronto designs and builds SDLs. One example of an SDL is a self-driving laboratory for adhesive material optimization. This autonomous laboratory combines a robot for preparing and testing adhesive bonds. 

What is a self-driving laboratory?

What is a self-driving lab? Self-driving labs (SDLs), or autonomous experimentation, combine robotics for automated experiments and data collection, with artificial intelligence (AI) systems that use these data to recommend follow-up experiments 1, 2, 3

Self-driving labs (SDLs) combine artificial intelligence (AI) with automated experiments to determine the next set of experiments. SDLs can potentially lead to a new scientific research paradigm where machines explore, interpret, and explain the world for human benefit. 

Here are some perspectives for self-driving labs in synthetic biology: 

• Efficiency SDLs can accelerate the pace of molecular and materials discovery. They can also reduce the time-to-solution through iterative hypothesis formulation, intelligent experiment selection, and automated testing. SDLs can achieve scientific objectives hundreds of times faster than automation alone. 
• Automation SDLs can automate inefficient, time-consuming, and laborious protein engineering campaigns. This allows researchers to focus on important downstream applications. 
• Synthetic biology SDLs can provide a unique opportunity because the genome provides a single target for affecting the wide repertoire of biological cell behavior. 

Synthetic biology aims to introduce engineering principles into the life sciences to improve the reliability of the “Design-Build-Test-Learn cycle”

A minimal working example (MWE) for a self-driving laboratory (SDL) is a setup that: 

• Costs less than $100 
• Takes up less than 1 ft2 of desk space 
• Takes less than 1 hour to set up 

Here are some core principles of a self-driving materials discovery lab: 

• Sending commands to hardware to adjust physical parameters 
• Receiving measured objective properties 
• Decision-making via active learning 

Here’s a protocol for setting up a Closed-loop Spectroscopy Lab: Light-mixing Demo (CLSLab:Light), a “Hello, World!” for a self-driving laboratory: 

• Use a Pico W microcontroller, LEDs, a light sensor, and Bayesian optimization

A self-driving lab (SDL) is a system that combines artificial intelligence (AI) and robotics to perform experiments and collect data. The AI systems then use the data to recommend follow-up experiments

Here are some characteristics of SDLs: 

• Automation: SDLs can automate repetitive tasks, freeing researchers to focus on creating new materials and molecules. 
• Continuous operation: SDLs can run 24/7 and make decisions on the fly. 
• Iterative learning: SDLs use experiment planning algorithms and automation to iteratively plan, execute, and learn from materials science experiments. 
• Closed-loop workflow: SDLs can significantly speed up material design. 

SDLs have the potential to increase the rate of scientific discovery and experimentation. For example, SDLs can accelerate the process of discovering and optimizing commercially viable materials for clean energy applications

Instead of researchers spending untold hours performing tedious trial and error experiments, self-driving lab technologies enable scientists to pre-define the desired properties of a material, leaving the lab to then work autonomously – using computational modelling to predict what molecular combinations will best suit …

Self-driving labs (SDLs) are level-3 autonomy systems that combine artificial intelligence (AI) and robotics to develop new materials. SDLs are described by their degree of independence from human intervention

Here are some examples of self-driving labs: 

• Polybot: A self-driving laboratory that automates aspects of electronic polymer research. 
• The Acceleration Consortium: An Institutional Strategic Initiative at the University of Toronto that designs and builds self-driving labs. 
• Cloud labs: Allow researchers to contract a self-driving lab on a pay-as-you go basis. 

SDLs have the potential to increase the rate of experimentation and scientific discovery.

Self-driving laboratories (SDLs) use artificial intelligence (AI) and automated robotic platforms to autonomously discover new materials. SDLs can run 24/7 and make decisions on the fly, freeing up researchers to focus on creating new molecules and materials

SDLs could usher in a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. For example, a mobile robot in a chemical lab combined with AI technologies could solve complex tasks, such as searching for the optimal photocatalytic system within the process of hydrogen production from water. 

The Aspuru-Guzik group sees in these laboratories the potential to increase the rate of experimentation and scientific discovery, which will eventually change the way we do science

To hasten the discovery time, researchers at Argonne have a new tool, a self-driving laboratory called Polybot that automates aspects of electronic polymer research and frees scientists’ time to work on tasks only humans can accomplish

Polybot is a self-driving laboratory that combines robotics and artificial intelligence to automate aspects of electronic polymer research. Polybot can reduce development time and cost, and potentially reduce the cost of complex projects from millions to thousands of dollars

Polybot’s automated system chooses a polymer solution recipe, prepares it, and prints it as a thin film at a selected speed and temperature. Polybot’s potential applications include speeding up the discovery of wearable biomedical devices and materials for better batteries

Polybot is a self-driving laboratory that uses AI and robotics to automate aspects of electronic polymer research. It’s one of several autonomous discovery labs being developed by Argonne and other research organizations

Polybot’s modular robotic platform uses a rotatable pneumatic gripper on a polar robotic arm to interact with objects. It can autonomously optimize the optical and electronic properties of thin-film materials by modifying the film composition and processing conditions. 

Polybot’s modular automated experimental features enable the experimental features of synthesis, processing, and characterizations in polymer electronics. 

Polymer electronics have shown unique advances in many emerging applications such as skin-like electronics, large-area printed energy devices, and neuromorphic computing devices.

Researchers are devising self-driving labs to do science in a surprisingly wide range of disciplines: in August, for example, a group at Boston University discovered the most energy-absorbing structure ever measured, which could find use in bike helmets and car crumple zones, after searching through possible designs …

Self-driving labs (SDLs) combine AI and automated experiments to decide the next set of experiments. They can run 24/7 and make decisions on the fly, freeing up researchers to focus on creating new materials and molecules

SDLs could accelerate the pace of materials and molecular discovery by 10–100X. They could also reduce the cost of complex projects from millions to thousands of dollars. 

Some say that the integration of AI, robotics, and digitalization could revolutionize R&D. Accelerated R&D could lead to breakthrough discoveries and products with the potential to create new markets and reshape entire industries

(Full article source google)

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