If you follow the directions in a cake recipe, you expect to end up with a nice fluffy cake. In Idaho Falls, though, the elevation can affect these results. When baked goods don’t turn out as expected, the troubleshooting begins. This happens in chemistry, too. Chemists must be able to account for how subtle changes or additions may affect the outcome for better or worse.
Chemists make their version of recipes, known as reactions, to create specific materials. These materials are essential ingredients to an array of products found in healthcare, farming, vehicles and other everyday products from diapers to diesel. When chemists develop new materials, they rely on information from previous experiments and predictions based on prior knowledge of how different starting materials interact with others and behave under specific conditions. There are a lot of assumptions, guesswork and experimentation in designing reactions using traditional methods. New computational methods like machine learning can help scientists better understand complex processes like chemical reactions. While it can be challenging for humans to pick out patterns hidden within the data from many different experiments, computers excel at this task.
Machine learning is an advanced computational tool where programmers give computers lots of data and minimal instructions about how to interpret it. Instead of incorporating human bias into the analysis, the computer is only instructed to pull out what it finds to be important from the data. This could be an image of a cat (if the input is all the photos on the internet) or information about how a chemical reaction proceeds through a series of steps, as is the case for a set of machine learning experiments that are ongoing at Idaho National Laboratory.