Recent Advances and Applications of Deep Learning Methods in Materials Science

Abstract

Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured

Machine learning in crystallography and structural science

“…Although the foundations of machine learning and deep learning (DL) came from the worlds of academic computing, mathematics and theories of the brain (McCulloch & Pitts, 1943;Rosenblatt, 1958), many of the early societal impacts were in commerce. However, physical scientists are now adopting these developments in the pursuit of their own science (Choudhary et al, 2021), and crystallography is no exception. It is therefore very timely to pull together the growing number of AI/ML papers that have been published in Acta Crystallographica (Sections A, B and D), IUCrJ and Journal of Synchrotron Radiation.…”