Figure credits: Pixabay

Chemical engineering researchers at North Carolina State University have developed a self-driven lab that utilizes an artificial intelligence technique called reinforcement learning to identify and optimize new complex multistep reaction routes for the synthesis of advanced functional materials and molecules. Named AlphaFlow, the system was tested in a proof-of-concept demonstration, where it found a more efficient way to produce high-quality semiconductor nanocrystals used in optical and photonic devices.

The system makes use of automated microfluidic devices and can conduct more experiments than 100 human chemists in the same period of time, while using less than 0.01% of the relevant chemicals. AlphaFlow’s AI model makes decisions on what experiment to conduct next based on the data it has developed from experiments it already ran and what it predicts the results of the next several experiments will be. The integration of AI and chemistry reduces the amount of time it takes to develop new chemistries by at least an order of magnitude, from months or years to hours.

AlphaFlow is capable of discovering a new chemical or optimizing the manufacturing process for a known chemical. AlphaFlow’s focus for optimization is on determining what amount of each precursor is needed, as well as the amount of time needed for each reaction, to reach optimal performance most efficiently.

The system can be modified to conduct any range of experiments that involve performing chemical reactions in solution. The researchers are now looking for partners in both the research community and the private sector to begin using AlphaFlow to address chemistry challenges. The system is open source, and the researchers think it is important to share high quality, reproducible, standardized, experimental data from both failures and successes to accelerate the discovery of new materials and chemical processes.

For more info: www.nature.com