Self-driving labs (SDLs) can reduce the time and cost of bringing advanced materials to market, from an average of 20 years and $100 million to as little as one year and $1 million.
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VIDEO LOOP: 1. ADA at the University of British Columbia (Curtis Berlinguette, Jason Hein, Alán Aspuru-Guzik); 2. Artificial Chemist synthesizes made-to-measure inoroganic perovskite quantum dots (Milad Abolhasani, NC State University); 3. Robotically reconfigurable flow chemistry platform performs multistep chemical syntheses planned in part by AI (Connor Coley, MIT); 4. Chemputer, a computer-driven automated chemistry lab (Lee Cronin, University of Glasgow); 5. Mobile robot chemist (Andy Cooper, University of Liverpool)
What are they?
Self-driving labs (SDLs), also sometimes referred to as materials acceleration platforms (MAPs), combine material science with the power of artificial intelligence, robotics, and advanced computing, to autonomously and rapidly design and test new materials.
How do they work?
The scope of materials discovery is theoretically enormous: the number of unique materials that can be synthesized exceeds the number of atoms in the universe. Conventional approaches are slow and expensive, allowing researchers to only explore a tiny subset of the materials that satisfy application-specific functionality.
Self-driving labs use artificial intelligence (AI) and computational modelling to predict which advanced materials or small molecules will have the properties (e.g. conductivity) required for a particular application. A robotic lab then uses these predictions to autonomously synthesize and tests for said properties. This data is then fed back into the AI system, so that it can learn from the results to generate a new, better slate of candidates. After rounds of predictions, syntheses, and tests, a winner emerges. By inverting the usual discovery process, this closed-loop technology allows scientists to first define the desired properties and then work backwards to develop whole families of new materials without tedious hours of trial and experiments in the lab.
CLOSED LOOP DESIGN
Automated chemical synthesis
Automated material characterization
New advanced materials
What’s the hype?
Digitizing the lab shortens materials development time and cost by an order of magnitude. This materials sciences revolution is expected to create new trillion-dollar markets over the next decade.
Materials are at the centre of nearly all the world’s major challenges, from pandemics and climate change to plastic pollution. New materials are required for higher-efficiency renewable energy and energy storage technologies, reusable and biodegradable materials, and to replace critical materials in limited supply or subject to supply disruptions.
For example, advanced materials represent up to 50% of the manufacturing cost of clean energy technology, and this is expected to increase to 80% in the near future. Realizing these technological solutions requires the discovery of novel advanced materials that are more efficient, longer lasting, less expensive, and more environmentally-friendly. As a foundational technology, MAPS can be applied to a wide range of areas that are critical to our socio-economic well-being and the planet. Clear parallels exist between the high cost and long timelines of materials development to genome sequencing prior to the advent of the Human Genome Project. New sequencing technologies led to the exponential decrease in the cost and time required to decode the genomes of living organisms. These developments revolutionized biology, created the discipline of genomics, and led to the explosion of the biotech industry.
In addition to discovery and design, the AC’s self-driving labs will push the frontiers of fundamental research in several fields—such as robotics, computer science and chemistry—to develop novel methodologies and tools. AC researchers will address fundamental challenges in deep learning algorithms and materials modelling, and practical issues of robotic control.
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