Supercomputers Pave the Way for New Machine Learning Approach

New deep learning models predict the interactions between atoms in organic molecules. These models, which were generated using supercomputers at the San Diego Supercomputer Center and the Los Alamos National Laboratory, help computational biologists and drug development researchers better understand and treat disease. According to a release issued earlier this month by the Los Alamos National Laboratory (LANL), researchers have developed a machine learning approach called transfer learning that lets them model novel materials by learning from data collected about millions of other compounds. The new approach can be applied to new molecules in milliseconds, enabling research into a far greater number of compounds over much longer timescales. The new technique, called ANI-1ccx potential, promises to advance the capabilities of researchers in many fields and improve the accuracy of machine learning-based potentials in future studies of metal alloys and detonation physics.