This is an age of machine learning algorithms! They can predict stock market fluctuations, control complex manufacturing processes, enable navigation for robots and driverless vehicles, and much more. Now, a group of researchers has found a way to predict and control chemical reactions with precision and speed.
The technique was developed by researchers at the NYU Tandon School of Engineering, tapping the capabilities of artificial intelligence, artificial neural networks, and infrared thermal imaging and it can allow chemical discoveries to take place quickly and with far less environmental waste than standard large-scale reactions.
“This system can reduce the decision-making process about certain chemical manufacturing processes from one year to a matter of weeks, saving tons of chemical waste and energy in the process,” said Ryan Hartman, lead author of the paper – “Artificial Neural Network Control of Thermoelectrically-Cooled Microfluidics using Computer Vision based on IR Thermography,” detailing the method in the journal Computers & Chemical Engineering.
How it works
Hartman and his team introduced a new class of miniaturized chemical reactors that brings reactions traditionally carried out in large-batch reactors with up to 100 liters of chemicals down to the microscale, using just microliters of fluid – a few small drops. They increased the utility of these reactors by pairing them with two additional technologies: infrared thermography — an imaging technique that captures a thermal map displaying changes in heat during a chemical reaction, and supervised machine learning — a discipline of artificial intelligence wherein an algorithm learns to interpret data based on inputs selected by researchers controlling the experiments.
Paired together, infrared thermography and machine learning allow researchers to capture changes in thermal energy during chemical reactions — as indicated by color changes on the thermal image — and to interpret these changes quickly. The research team is the first to train an artificial neural network to control and interpret infrared thermal images of a thermoelectrically cooled microfluidic device.
The potential impacts on both innovation and sustainability are significant. Large chemical companies may screen hundreds of catalysts while developing new polymers, for example, and each reaction can require more than 100 liters of chemicals and 24 hours or longer. Screening that a number of catalysts using current laboratory processes can take a year. Using Hartman’s approach, the entire process can be accomplished in weeks, with exponentially less waste and energy usage. Hartman estimates that a single industrial hood used to control fumes during large-scale chemical testing uses as much energy per year as the average U.S. home.
Notably, early this year, another group of researchers at Princeton University and Spencer Dreher of Merck Research Laboratories found a way to predict reaction yields accurately while varying up to four reaction components by using an application of artificial intelligence known as machine learning. They have turned their method into software that they have made available to other chemists. They published their research in February in the journal Science.
In July 2018, Lee Cronin’s lab at the University of Glasgow, UK, also created an organic synthesis robotic AI system that can quickly explore the reactivity of a set of reagents from the bottom-up with no specific target. The autonomous system was able to predict with 86% accuracy the reactivity of the remaining 90% of reactions. The robot can perform up to 36 experiments per day – around 10 times more than a human.