“We use this moving window of prior action steps and predicted outcomes of future action sequences to inform AlphaFlow’s decision making. It’s extremely efficient.”ĪlphaFlow’s AI model makes decisions on what experiment to conduct next based on two things: the data it has developed from experiments it already ran and what it predicts the results of the next several experiments will be. It effectively miniaturizes the experiments and performs the same laboratory operations that would require an entire wet chemistry lab in a suitcase-sized end-to-end experimental platform. We’ve shown that AlphaFlow can conduct more experiments than 100 human chemists in the same period of time, while using less than 0.01% of the relevant chemicals. “Our system, called AlphaFlow, makes use of an artificial intelligence technique called reinforcement learning that – when coupled with automated microfluidic devices – expedites the material discovery process. “If a complex chemistry includes dozens of parameters, it might take decades to develop a new target material or a more efficient way to produce a desired chemical. “Progress in materials and molecular discovery is slow, because conventional techniques for discovering new chemistries rely on varying one parameter at a time using siloed operations in chemistry and materials science labs,” says Milad Abolhasani, corresponding author of a paper on the work and a professor of chemical and biomolecular engineering at North Carolina State University. In a proof-of-concept demonstration, the system found a more efficient way to produce high-quality semiconductor nanocrystals that are used in optical and photonic devices. Matt Shipman team of chemical engineering researchers has developed a self-driven lab that is capable of identifying and optimizing new complex multistep reaction routes for the synthesis of advanced functional materials and molecules.
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