The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world’s most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL’s Thomas Potok, includes Robert Patton, Chris Symons, Steven Young and Catherine Schuman.
While deep learning has long been used to classify relatively simple data such as photographs, today’s scientific data presents a much greater challenge because of its size and complexity. Deep learning offers the potential to truly change the way in which researchers use massive datasets to solve challenges spanning the scientific spectrum.
For example, neutron scattering data collected at ORNL’s Spallation Neutron Source contain rich scientific information about structure and dynamics of materials under investigation, and deep learning could help researchers better understand the link between experimental data and materials properties. “This understanding can help scientists build and support new scientific theories, and help to design better materials,” Potok said.