The U.S. Department of Energy's Argonne National Laboratory is working to use machine learning and artificial intelligence to build a better battery. Should the DOE's efforts prove fruitful, it could be a positive development for the medical device industry, where batteries have proven to be a technological stumbling block when it comes to device miniaturization. Argonne researchers created a database of approximately 133,000 small organic molecules that could form the basis of battery electrolytes with a computationally intensive model called G4MP2, which represents 166 billion larger molecules that scientists wanted to probe for electrolyte candidates, according to a news release. The researchers applied a machine-learning algorithm to relate the known structures from the small data set to more coarsely modeled structures from the larger set, using a less computationally taxing modeling framework based on density functional theory. It is less accurate than G4MP2, but density functional theory provides a good approximation, according to the DOE.