We learn new skills by repetition and reinforcement learning. Through trial and error, we repeat actions leading to good outcomes, try to avoid bad outcomes and seek to improve those in between. Researchers are now designing algorithms based on a form of artificial intelligence that uses reinforcement learning. They are applying them to automate chemical synthesis, drug discovery and even play games like chess and Go.
Scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory have developed a reinforcement learning algorithm for yet another application. It is for modeling the properties of materials at the atomic and molecular scale and should greatly speed up materials discovery.
Like humans, this algorithm “learns” problem solving from its mistakes and successes. But it does so without human intervention.