Traditionally, scientists have discovered materials through a combination of intuition, pluck, and just plain luck. The approach led to many dead ends, but it also produced key innovations, including the components of the lithium-ion battery, which Crabtree calls “the best battery we’ve ever had.”
Starting in the late 20th century, computers enabled scientists to simulate structures and properties of molecules and materials and synthesize only the most promising ones, saving time and money. High-throughput screening further enabled the testing of dozens or hundreds of compounds in quick succession.
But these methods still face key limitations, namely, scientists’ imagination and understanding. The landscape of possible arrangements of elements in a molecule or crystal lattice is unimaginably vast, and scientists have studied only slivers of that landscape. “We’ve barely scratched the surface,” says Venkat Viswanathan, a materials scientist at Carnegie Mellon University in Pennsylvania.
Enter artificial intelligence. AI methods such as machine learning can plumb enormous datasets for subtle correlations, or trends, that might elude humans. A familiar application is facial recognition, in which algorithms analyze millions of photos to learn how to spot subtle facial features that identify a person.
In a materials context, one can train a machine-learning algorithm to seek arrangements of atoms that yield a desired material property or function. Obtaining these structure-function correlations could allow “inverse design”—a long-held goal in materials science in which the optimum material can be found for a selected function.
There is a problem, however: Materials science lacks the massive training datasets that are available for facial recognition and other common AI applications. “Materials science is by design very sparse in data,” says Kristin Persson, a physicist at Lawrence Berkeley National Laboratory in California who runs the Materials Project, the world’s largest public materials database. Synthesizing and fully characterizing a material is painstaking and time consuming, so databases of materials often contain at most a few hundred entries for a particular material property.
But with larger troves of materials data rapidly coming online and new methods that can generate large volumes of data quickly, many researchers feel materials science is ripe for an AI revolution. Climate change could be an area where that revolution delivers big results.