A good machine-learning algorithm is a powerful research accelerator. Pair it with a computer simulation and it can sniff out mathematical shortcuts through the program, propelling scientists to faster insights about the effects of drugs on cells or the potential of rocket engines to send humankind to Mars and beyond.
New research is putting this tool into the hands of scientists around the world. In a machine learning paper recently published in the journal npj Computational Materials, a team of researchers from Sandia National Laboratories and Brown University have introduced a universal way to accelerate virtually any kind of simulation.
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