Using Transfer Learning to Overcome the Barriers Facing Machine Learning in Materials Science - News

Machine learning's ability to perform intellectually demanding tasks across various fields, materials science included, has caused it to receive considerable attention. Many believe that it could be used to unlock major time and cost savings in the development of new materials. The growing demand for the use of machine learning to derive fast-to-evaluate surrogate models of material properties has prompted scientists at the National Institute for Materials Science in Tsukuba, Japan, to demonstrate that it could be the key driver of the "next frontier" of materials science in recently published research. To learn, machines rely on processing data using both supervised and unsupervised learning. With no data, however, there is nothing to learn from.